From 221aec3d0d2e2f8a5037883c212f3c69d3c993f7 Mon Sep 17 00:00:00 2001 From: Keith Battocchi Date: Fri, 12 Aug 2022 16:08:27 -0400 Subject: [PATCH] Create panel subpackage --- README.md | 2 +- doc/reference.rst | 2 +- doc/spec/estimation/dynamic_dml.rst | 6 +- econml/dynamic/dml/__init__.py | 154 +- econml/panel/__init__.py | 4 + econml/panel/dml/__init__.py | 20 + econml/{dynamic => panel}/dml/_dml.py | 2 +- econml/panel/utilities.py | 39 + econml/tests/test_dynamic_dml.py | 4 +- ... at Microsoft via Short-Term Proxies.ipynb | 1885 ++++++++--------- ...mic Double Machine Learning Examples.ipynb | 6 +- 11 files changed, 1158 insertions(+), 966 deletions(-) create mode 100644 econml/panel/__init__.py create mode 100644 econml/panel/dml/__init__.py rename econml/{dynamic => panel}/dml/_dml.py (99%) create mode 100644 econml/panel/utilities.py diff --git a/README.md b/README.md index 544fca02c..68bcd75aa 100644 --- a/README.md +++ b/README.md @@ -173,7 +173,7 @@ To install from source, see [For Developers](#for-developers) section below. Dynamic Double Machine Learning (click to expand) ```Python - from econml.dynamic.dml import DynamicDML + from econml.panel.dml import DynamicDML # Use defaults est = DynamicDML() # Or specify hyperparameters diff --git a/doc/reference.rst b/doc/reference.rst index 5df1fcdeb..767f9c48c 100644 --- a/doc/reference.rst +++ b/doc/reference.rst @@ -120,7 +120,7 @@ Dynamic Double Machine Learning .. autosummary:: :toctree: _autosummary - econml.dynamic.dml.DynamicDML + econml.panel.dml.DynamicDML .. _policy_api: diff --git a/doc/spec/estimation/dynamic_dml.rst b/doc/spec/estimation/dynamic_dml.rst index b92f319d8..055359b34 100644 --- a/doc/spec/estimation/dynamic_dml.rst +++ b/doc/spec/estimation/dynamic_dml.rst @@ -46,7 +46,7 @@ characteristics :math:`X` of the treated samples, then one can use this method. .. testcode:: - from econml.dynamic.dml import DynamicDML + from econml.panel.dml import DynamicDML est = DynamicDML() est.fit(y_dyn, T_dyn, X=X_dyn, W=W_dyn, groups=groups) @@ -57,7 +57,7 @@ Class Hierarchy Structure In this library we implement variants of several of the approaches mentioned in the last section. The hierarchy structure of the implemented CATE estimators is as follows. - .. inheritance-diagram:: econml.dynamic.dml.DynamicDML + .. inheritance-diagram:: econml.panel.dml.DynamicDML :parts: 1 :private-bases: :top-classes: econml._OrthoLearner, econml._cate_estimator.LinearModelFinalCateEstimatorMixin @@ -83,7 +83,7 @@ Below we give a brief description of each of these classes: .. testcode:: - from econml.dynamic.dml import DynamicDML + from econml.panel.dml import DynamicDML est = DynamicDML() est.fit(y_dyn, T_dyn, X=X_dyn, W=W_dyn, groups=groups) diff --git a/econml/dynamic/dml/__init__.py b/econml/dynamic/dml/__init__.py index a95579da7..fd22d5bcb 100755 --- a/econml/dynamic/dml/__init__.py +++ b/econml/dynamic/dml/__init__.py @@ -15,6 +15,156 @@ ``_, 2021. """ -from ._dml import DynamicDML +import econml.panel.dml +from econml.utilities import deprecated -__all__ = ["DynamicDML"] + +@deprecated("The DynamicDML class has been moved to econml.panel.dml.DynamicDML; " + "an upcoming release will remove the econml.panel package, please update references to the new location") +def DynamicDML(*, + model_y='auto', model_t='auto', + featurizer=None, + fit_cate_intercept=True, + linear_first_stages=False, + discrete_treatment=False, + categories='auto', + cv=2, + mc_iters=None, + mc_agg='mean', + random_state=None): + """CATE estimator for dynamic treatment effect estimation. + + This estimator is an extension of the Double ML approach for treatments assigned sequentially + over time periods. + + The estimator is a special case of an :class:`_OrthoLearner` estimator, so it follows the two + stage process, where a set of nuisance functions are estimated in the first stage in a crossfitting + manner and a final stage estimates the CATE model. See the documentation of + :class:`._OrthoLearner` for a description of this two stage process. + + Parameters + ---------- + model_y: estimator or 'auto', optional (default is 'auto') + The estimator for fitting the response to the features. Must implement + `fit` and `predict` methods. + If 'auto' :class:`.WeightedLassoCV`/:class:`.WeightedMultiTaskLassoCV` will be chosen. + + model_t: estimator or 'auto', optional (default is 'auto') + The estimator for fitting the treatment to the features. + If estimator, it must implement `fit` and `predict` methods; + If 'auto', :class:`~sklearn.linear_model.LogisticRegressionCV` will be applied for discrete treatment, + and :class:`.WeightedLassoCV`/:class:`.WeightedMultiTaskLassoCV` + will be applied for continuous treatment. + + featurizer : :term:`transformer`, optional, default None + Must support fit_transform and transform. Used to create composite features in the final CATE regression. + It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). + If featurizer=None, then CATE is trained on X. + + fit_cate_intercept : bool, optional, default True + Whether the linear CATE model should have a constant term. + + linear_first_stages: bool + Whether the first stage models are linear (in which case we will expand the features passed to + `model_y` accordingly) + + discrete_treatment: bool, optional (default is ``False``) + Whether the treatment values should be treated as categorical, rather than continuous, quantities + + categories: 'auto' or list, default 'auto' + The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). + The first category will be treated as the control treatment. + + cv: int, cross-validation generator or an iterable, optional (Default=2) + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - :term:`CV splitter` + - An iterable yielding (train, test) splits as arrays of indices. + Iterables should make sure a group belongs to a single split. + + For integer/None inputs, :class:`~sklearn.model_selection.GroupKFold` is used + + Unless an iterable is used, we call `split(X, T, groups)` to generate the splits. + + mc_iters: int, optional (default=None) + The number of times to rerun the first stage models to reduce the variance of the nuisances. + + mc_agg: {'mean', 'median'}, optional (default='mean') + How to aggregate the nuisance value for each sample across the `mc_iters` monte carlo iterations of + cross-fitting. + + random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; + If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used + by :mod:`np.random`. + + Examples + -------- + A simple example with default models: + + .. testcode:: + :hide: + + import numpy as np + np.set_printoptions(suppress=True) + + .. testcode:: + + from econml.panel.dml import DynamicDML + + np.random.seed(123) + + n_panels = 100 # number of panels + n_periods = 3 # number of time periods per panel + n = n_panels * n_periods + groups = np.repeat(a=np.arange(n_panels), repeats=n_periods, axis=0) + X = np.random.normal(size=(n, 1)) + T = np.random.normal(size=(n, 2)) + y = np.random.normal(size=(n, )) + est = DynamicDML() + est.fit(y, T, X=X, W=None, groups=groups, inference="auto") + + >>> est.const_marginal_effect(X[:2]) + array([[-0.336..., -0.048..., -0.061..., 0.042..., -0.204..., + 0.00667271], + [-0.101..., 0.433..., 0.054..., -0.217..., -0.101..., + -0.159...]]) + >>> est.effect(X[:2], T0=0, T1=1) + array([-0.601..., -0.091...]) + >>> est.effect(X[:2], T0=np.zeros((2, n_periods*T.shape[1])), T1=np.ones((2, n_periods*T.shape[1]))) + array([-0.601..., -0.091...]) + >>> est.coef_ + array([[ 0.112...], + [ 0.231...], + [ 0.055...], + [-0.125...], + [ 0.049...], + [-0.079...]]) + >>> est.coef__interval() + (array([[-0.063...], + [-0.009...], + [-0.114...], + [-0.413...], + [-0.117...], + [-0.262...]]), array([[0.289...], + [0.471...], + [0.225...], + [0.163...], + [0.216...], + [0.103...]])) + """ + return econml.panel.dml.DynamicDML( + model_y=model_y, model_t=model_t, + featurizer=featurizer, + fit_cate_intercept=fit_cate_intercept, + linear_first_stages=linear_first_stages, + discrete_treatment=discrete_treatment, + categories=categories, + cv=cv, + mc_iters=mc_iters, + mc_agg=mc_agg, + random_state=random_state) diff --git a/econml/panel/__init__.py b/econml/panel/__init__.py new file mode 100644 index 000000000..34e76781c --- /dev/null +++ b/econml/panel/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. + +__all__ = ["dml"] diff --git a/econml/panel/dml/__init__.py b/econml/panel/dml/__init__.py new file mode 100644 index 000000000..6fb1669e3 --- /dev/null +++ b/econml/panel/dml/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. + +"""Double Machine Learning for Dynamic Treatment Effects. + +A Double/Orthogonal machine learning approach to estimation of heterogeneous +treatment effect in the dynamic treatment regime. For the theoretical +foundations of these methods see: [dynamicdml]_. + +References +---------- + +.. [dynamicdml] Greg Lewis and Vasilis Syrgkanis. + Double/Debiased Machine Learning for Dynamic Treatment Effects. + ``_, 2021. +""" + +from ._dml import DynamicDML + +__all__ = ["DynamicDML"] diff --git a/econml/dynamic/dml/_dml.py b/econml/panel/dml/_dml.py similarity index 99% rename from econml/dynamic/dml/_dml.py rename to econml/panel/dml/_dml.py index 94605fd10..fa9f948b2 100644 --- a/econml/dynamic/dml/_dml.py +++ b/econml/panel/dml/_dml.py @@ -408,7 +408,7 @@ class DynamicDML(LinearModelFinalCateEstimatorMixin, _OrthoLearner): .. testcode:: - from econml.dynamic.dml import DynamicDML + from econml.panel.dml import DynamicDML np.random.seed(123) diff --git a/econml/panel/utilities.py b/econml/panel/utilities.py new file mode 100644 index 000000000..91056968e --- /dev/null +++ b/econml/panel/utilities.py @@ -0,0 +1,39 @@ + +import numpy as np + + +def long(x): + """ + Reshape panel data to long format, i.e. (n_units * n_periods, d_x) or (n_units * n_periods,) + + Parameters + ---------- + x : array-like + Panel data in wide format + + Returns + ------- + arr : array-like + Reshaped panel data in long format""" + n_units = x.shape[0] + n_periods = x.shape[1] + if np.ndim(x) == 2: + return x.reshape(n_units * n_periods) + else: + return x.reshape(n_units * n_periods, -1) + + +def wide(x): + """Reshape panel data to wide format, i.e. (n_units, n_periods * d_x) or (n_units, n_periods,) + + Parameters + ---------- + x : array-like + Panel data in long format + + Returns + ------- + arr : array-like + Reshaped panel data in wide format""" + n_units = x.shape[0] + return x.reshape(n_units, -1) diff --git a/econml/tests/test_dynamic_dml.py b/econml/tests/test_dynamic_dml.py index 155934d42..4a24aca56 100644 --- a/econml/tests/test_dynamic_dml.py +++ b/econml/tests/test_dynamic_dml.py @@ -8,8 +8,8 @@ from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, PolynomialFeatures from sklearn.linear_model import (LinearRegression, LassoCV, Lasso, MultiTaskLasso, MultiTaskLassoCV, LogisticRegression) -from econml.dynamic.dml import DynamicDML -from econml.dynamic.dml._dml import _get_groups_period_filter +from econml.panel.dml import DynamicDML +from econml.panel.dml._dml import _get_groups_period_filter from econml.inference import BootstrapInference, EmpiricalInferenceResults, NormalInferenceResults from econml.utilities import shape, hstack, vstack, reshape, cross_product import econml.tests.utilities # bugfix for assertWarns diff --git a/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb b/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb index 35882ad10..509fe07c6 100644 --- a/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb +++ b/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb @@ -1,955 +1,934 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Long-Term Return-on-Investment at Microsoft via Short-Term Proxies\n", - "\n", - "\n", - "Policy makers typically face the problem of wanting to estimate the treatment effect of some new incentives on long-run downstream interests. However, we only have historical data of older treatment options, and we haven't seen the long-run play out yet. We assume access to a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies. Our work combines three major recent techniques in the causal machine learning literature: **surrogate indices**, **dynamic treatment effect estimation** and **double machine learning**, in a unified\n", - "pipeline. For more details, see this paper [here](https://arxiv.org/pdf/2103.08390.pdf).\n", - "\n", - "In this case study, we will show you how to apply this unified pipeline to a ROI estimation problem at Microsoft. These methodologies have already been implemented into our [EconML](https://aka.ms/econml) library and you could do it with only a few lines of code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary\n", - "\n", - "1. [Background](#Background)\n", - "2. [Data](#Data)\n", - "3. [Do Dynamic Adjustment with EconML](#Do-Dynamic-Adjustment-with-EconML)\n", - "4. [Train Surrogate Index](#Train-Surrogate-Index)\n", - "5. [Run DML to Learn ROI with EconML](#Run-DML-to-Learn-ROI-with-EconML)\n", - "6. [Model Evaluation](#Model-Evaluation)\n", - "7. [Extensions -- Including Heterogeneity in Effect](#Extensions----Including-Heterogeneity-in-Effect)\n", - "8. [Conclusions](#Conclusions)" - ] - }, - { - "attachments": { - "causal_graph.PNG": { - "image/png": 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" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Long-Term Return-on-Investment at Microsoft via Short-Term Proxies\n", + "\n", + "\n", + "Policy makers typically face the problem of wanting to estimate the treatment effect of some new incentives on long-run downstream interests. However, we only have historical data of older treatment options, and we haven't seen the long-run play out yet. We assume access to a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies. Our work combines three major recent techniques in the causal machine learning literature: **surrogate indices**, **dynamic treatment effect estimation** and **double machine learning**, in a unified\n", + "pipeline. For more details, see this paper [here](https://arxiv.org/pdf/2103.08390.pdf).\n", + "\n", + "In this case study, we will show you how to apply this unified pipeline to a ROI estimation problem at Microsoft. These methodologies have already been implemented into our [EconML](https://aka.ms/econml) library and you could do it with only a few lines of code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary\n", + "\n", + "1. [Background](#Background)\n", + "2. [Data](#Data)\n", + "3. [Do Dynamic Adjustment with EconML](#Do-Dynamic-Adjustment-with-EconML)\n", + "4. [Train Surrogate Index](#Train-Surrogate-Index)\n", + "5. [Run DML to Learn ROI with EconML](#Run-DML-to-Learn-ROI-with-EconML)\n", + "6. [Model Evaluation](#Model-Evaluation)\n", + "7. [Extensions -- Including Heterogeneity in Effect](#Extensions----Including-Heterogeneity-in-Effect)\n", + "8. [Conclusions](#Conclusions)" + ] + }, + { + "attachments": { + "causal_graph.PNG": { + "image/png": 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" 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Background\n", + "\n", + "Microsoft provides multiple montetary and resource investments to enterprice customers in support of products adoption, the sales manager would like to know which of these programs (\"investments\") are more successful than others? Specifically, we are interested in identifying the average treatment effect of each investment at some period $t$, on the cumulative outcome in the subsequent $m$ months. \n", + "\n", + "There are a few challenges to answer this question. First of all, we haven't fully observed the long-term revenue yet and we don't want to wait that long to evaluate a program. In addition, a careful causal modeling is required to correctly attribute the long-term ROI of multiple programs in a holistic manner, avoiding the biased estimate coming from confounding effect or double counting issues. \n", + "\n", + "The causal graph below shows how to frame this problem:\n", + "\n", + "![causal_graph.PNG](attachment:causal_graph.PNG)\n", + "\n", + "**Methodology:** Our proposed adjusted surrogate index approach could address all the chanllenges above by assuming the long-term effect is channeled through some short-term observed surrogates and employing a dynamic adjustment step (`DynamicDML`) to the surrogate model in order to get rid of the effect from future investment, finally applying double machine learning (`DML`) techniques to estimate the ROI. \n", + "\n", + "The pipeline below tells you how to solve this problem step by step:\n", + "![pipeline.PNG](attachment:pipeline.PNG)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "from econml.data.dynamic_panel_dgp import SemiSynthetic\n", + "from sklearn.linear_model import LassoCV, MultiTaskLassoCV\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data\n", + "\n", + "The **semi-synthetic data*** is comprised of 4 components:\n", + " * **Surrogates:** short-term metrics that could represent long-term revenue\n", + " * **Treatments:** different types of monetary investments to the end customers\n", + " * **Outcomes:** cumulative long-term revenue\n", + " * **Controls:** lagged surrogates and treatments, other time-invariant controls (e.g. demographics)\n", + "\n", + "To build the semi-synthetic data we estimate a series of moments from a real-world dataset: a full covariance matrix of\n", + "all surrogates, treatments, and controls in one period and a series of linear prediction models (lassoCV) of each surrogate and\n", + "treatment on a set of 6 lags of each treatment, 6 lags of each surrogate, and time-invariant controls. Using these values, we draw new parameters from distributions matching the key characteristics of each family of parameters. Finally, we use these new\n", + "parameters to simulate surrogates, treatments, and controls by drawing a set of initial values from the covariance matrix and\n", + "forward simulating to match intertemporal relationships from the transformed prediction models. We use one surrogate to be the outcome of interests. Then we consider the effect of each treatment in period $t$ on the cumulative sum of outcome from following 4 periods. We can calculate the true treatment effects in the semi-synthetic data as a function of parameters from the linear prediction models.\n", + "\n", + "The input data is in a **panel format**. Each panel corresponds to one company and the different rows in a panel correspond to different time period. \n", + "\n", + "Example:\n", + "\n", + "||Company|Year|Features|Controls/Surrogates|T1|T2|T3|AdjRev|\n", + "|---|---|---|---|---|---|---|---|---|\n", + "|1|A|2018|...|...|\\$1,000|...|...|\\$10,000|\n", + "|2|A|2019|...|...|\\$2,000|...|...|\\$12,000|\n", + "|3|A|2020|...|...|\\$3,000|...|...|\\$15,000|\n", + "|4|A|2021|...|...|\\$3,000|...|...|\\$18,000|\n", + "|5|B|2018|...|...|\\$0|...|...|\\$5,000|\n", + "|6|B|2019|...|...|\\$1,000|...|...|\\$10,000|\n", + "|7|B|2020|...|...|\\$0|...|...|\\$7,000|\n", + "|8|B|2021|...|...|\\$1,200|...|...|\\$12,000|\n", + "|9|C|2018|...|...|\\$1,000|...|...|\\$20,000|\n", + "|10|C|2019|...|...|\\$1,500|...|...|\\$25,000|\n", + "|11|C|2020|...|...|\\$500|...|...|\\$18,000|\n", + "|12|C|2021|...|...|\\$500|...|...|\\$20,000|\n", + " \n", + " **For confidentiality reason, the data used in this case study is synthetically generated and the feature distributions don't exactly correspond to real distributions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate historical dataset (training purpose)\n", + "np.random.seed(43)\n", + "dgp = SemiSynthetic()\n", + "dgp.create_instance()\n", + "n_periods = 4\n", + "n_units = 5000\n", + "n_treatments = dgp.n_treatments\n", + "random_seed = 43\n", + "thetas = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", + "\n", + "panelX, panelT, panelY, panelGroups, true_effect = dgp.gen_data(\n", + " n_units, n_periods, thetas, random_seed\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome shape: (5000, 4)\n", + "Treatment shape: (5000, 4, 3)\n", + "Controls shape: (5000, 4, 71)\n" + ] + } + ], + "source": [ + "# print panel data shape\n", + "print(\"Outcome shape: \", panelY.shape)\n", + "print(\"Treatment shape: \", panelT.shape)\n", + "print(\"Controls shape: \", panelX.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# generate new dataset (testing purpose)\n", + "thetas_new = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", + "panelXnew, panelTnew, panelYnew, panelGroupsnew, true_effect_new = dgp.gen_data(\n", + " n_units, n_periods, thetas_new, random_seed\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n" + ] + } + ], + "source": [ + "# print true long term effect\n", + "true_longterm_effect = np.sum(true_effect_new, axis=0)\n", + "print(\"True Long-term Effect for each investment: \", true_longterm_effect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Do Dynamic Adjustment with EconML\n", + "From the causal graph above, we could see we want to first remove the effects of future incentives from the historical outcomes to create an **adjusted long-term revenue** as if those future incentives never happened.\n", + "\n", + "EconML's `DynamicDML` estimator is an extension of Double Machine Learning approach to **dynamically estimate the period effect of treatments assigned sequentially over time period**. In this scenario, it could help us to adjust the cumulative revenue by subtracting the period effect of all of the investments after the target investment.\n", + "\n", + "For more details about `DynamicDML`, please read this [paper](https://arxiv.org/pdf/2002.07285.pdf). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00],\n", + " [2.000e+00, 2.000e+00, 2.000e+00, 2.000e+00],\n", + " ...,\n", + " [4.997e+03, 4.997e+03, 4.997e+03, 4.997e+03],\n", + " [4.998e+03, 4.998e+03, 4.998e+03, 4.998e+03],\n", + " [4.999e+03, 4.999e+03, 4.999e+03, 4.999e+03]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "panelGroups" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.35952949 1.92451605 0.34684417]\n", + "[0.74662029 1.13138969 0.25069193 1.30585143 1.79051531 0.34597602]\n", + "[0.46734394 0.74952179 0.16292026 0.67056612 0.92299133 0.23686006\n", + " 1.36311063 1.91659314 0.34728767]\n" + ] + } + ], + "source": [ + "# on historical data construct adjusted outcomes\n", + "from econml.panel.dml import DynamicDML\n", + "from econml.panel.utilities import long, wide\n", + "\n", + "panelYadj = panelY.copy()\n", + "\n", + "est = DynamicDML(\n", + " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=2\n", + ")\n", + "for t in range(1, n_periods): # for each target period 1...m\n", + " # learn period effect for each period treatment on target period t\n", + " est.fit(\n", + " long(panelY[:, 1 : t + 1]),\n", + " long(panelT[:, 1 : t + 1, :]), # reshape data to long format\n", + " X=None,\n", + " W=long(panelX[:, 1 : t + 1, :]),\n", + " groups=long(panelGroups[:, 1 : t + 1]),\n", + " )\n", + " print(est.intercept_)\n", + " # remove effect of observed treatments\n", + " T1 = wide(panelT[:, 1 : t + 1, :])\n", + " panelYadj[:, t] = panelY[:, t] - est.effect(\n", + " T0=np.zeros_like(T1), T1=T1\n", + " ) # reshape data to wide format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train Surrogate Index\n", + "Once we have the adjusted outcome, we'd like to train any ML model to learn the relationship between short-term surrogates and long-term revenue from the historical dataset, assuming the treatment effect of investments on long-term revenue could **only** go through short-term surrogates, and the **relationship keeps the same** between the historical dataset and the new dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 9;\n", + " var nbb_formatted_code = \"# train surrogate index on historical dataset\\nXS = np.hstack(\\n [panelX[:, 1], panelYadj[:, :1]]\\n) # concatenate controls and surrogates from historical dataset\\nTotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\\nadjusted_proxy_model = LassoCV().fit(\\n XS, TotalYadj\\n) # train proxy model from historical dataset\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train surrogate index on historical dataset\n", + "XS = np.hstack(\n", + " [panelX[:, 1], panelYadj[:, :1]]\n", + ") # concatenate controls and surrogates from historical dataset\n", + "TotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\n", + "adjusted_proxy_model = LassoCV().fit(\n", + " XS, TotalYadj\n", + ") # train proxy model from historical dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 10;\n", + " var nbb_formatted_code = \"# predict new long term revenue\\nXSnew = np.hstack(\\n [panelXnew[:, 1], panelYnew[:, :1]]\\n) # concatenate controls and surrogates from new dataset\\nsindex_adj = adjusted_proxy_model.predict(XSnew)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# predict new long term revenue\n", + "XSnew = np.hstack(\n", + " [panelXnew[:, 1], panelYnew[:, :1]]\n", + ") # concatenate controls and surrogates from new dataset\n", + "sindex_adj = adjusted_proxy_model.predict(XSnew)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run DML to Learn ROI with EconML\n", + "Finally we will call `LinearDML` estimator from EconML to learn the treatment effect of multiple investments on the adjusted surrogate index in new dataset. `LinearDML` is a two stage machine learning models for estimating **(heterogeneous) treatment effects** when all potential confounders are observed, it leverages the machine learning power to deal with **high dimensional dataset** and still be able to construct **confidence intervals**. \n", + "\n", + "For more details, please read this [paper](https://arxiv.org/pdf/1608.00060.pdf). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", + "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858
cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733
cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " CATE Intercept Results \n", + "=======================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-----------------------------------------------------------------------\n", + "cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858\n", + "cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733\n", + "cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507\n", + "-----------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 11;\n", + " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nadjsurr_est.summary(alpha=0.05)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# learn treatment effect on surrogate index on new dataset\n", + "from econml.dml import LinearDML\n", + "\n", + "adjsurr_est = LinearDML(\n", + " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", + ")\n", + "# fit treatment_0 on total revenue from new dataset\n", + "adjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", + "# print treatment effect summary\n", + "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", + "adjsurr_est.summary(alpha=0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 12;\n", + " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\nadjsurr_point_est = adjsurr_est.intercept_\\nadjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# save the treatment effect and confidence interval\n", + "adjsurr_point_est = adjsurr_est.intercept_\n", + "adjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Evaluation\n", + "Now we want to compare the proposed **adjusted surrogate index** approach with estimation from realized long-term outcome. Below we train another `LinearDML` model on the realized cumulative revenue directly, without any adjustment. And then we visualize the two models output, comparing with the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", + "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304
cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004
cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " CATE Intercept Results \n", + "=======================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-----------------------------------------------------------------------\n", + "cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304\n", + "cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004\n", + "cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745\n", + "-----------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 13;\n", + " var nbb_formatted_code = \"# learn treatment effect on direct outcome\\nfrom econml.dml import LinearDML\\n\\ndirect_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\ndirect_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\ndirect_est.summary(alpha=0.05)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# learn treatment effect on direct outcome\n", + "from econml.dml import LinearDML\n", + "\n", + "direct_est = LinearDML(\n", + " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", + ")\n", + "# fit treatment_0 on total revenue from new dataset\n", + "direct_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", + "# print treatment effect summary\n", + "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", + "direct_est.summary(alpha=0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 14;\n", + " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\ndirect_point_est = direct_est.intercept_\\ndirect_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# save the treatment effect and confidence interval\n", + "direct_point_est = direct_est.intercept_\n", + "direct_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'Error bar plot of treatment effect from different models')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 15;\n", + " var nbb_formatted_code = \"# plot the error bar plot of different models\\nplt.figure(figsize=(18, 6))\\nplt.subplot(1, 2, 1)\\n\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\n\\nplt.subplot(1, 2, 2)\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments) + 0.04,\\n direct_point_est,\\n yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\\n fmt=\\\"o\\\",\\n label=\\\"Direct Model\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\nplt.suptitle(\\\"Error bar plot of treatment effect from different models\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the error bar plot of different models\n", + "plt.figure(figsize=(18, 6))\n", + "plt.subplot(1, 2, 1)\n", + "\n", + "plt.errorbar(\n", + " np.arange(n_treatments) - 0.04,\n", + " true_longterm_effect,\n", + " fmt=\"o\",\n", + " alpha=0.6,\n", + " label=\"Ground truth\",\n", + ")\n", + "plt.errorbar(\n", + " np.arange(n_treatments),\n", + " adjsurr_point_est,\n", + " yerr=(\n", + " adjsurr_conf_int_ub - adjsurr_point_est,\n", + " adjsurr_point_est - adjsurr_conf_int_lb,\n", + " ),\n", + " fmt=\"o\",\n", + " label=\"Adjusted Surrogate Index\",\n", + ")\n", + "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", + "plt.ylabel(\"Effect\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.errorbar(\n", + " np.arange(n_treatments) - 0.04,\n", + " true_longterm_effect,\n", + " fmt=\"o\",\n", + " alpha=0.6,\n", + " label=\"Ground truth\",\n", + ")\n", + "plt.errorbar(\n", + " np.arange(n_treatments),\n", + " adjsurr_point_est,\n", + " yerr=(\n", + " adjsurr_conf_int_ub - adjsurr_point_est,\n", + " adjsurr_point_est - adjsurr_conf_int_lb,\n", + " ),\n", + " fmt=\"o\",\n", + " label=\"Adjusted Surrogate Index\",\n", + ")\n", + "plt.errorbar(\n", + " np.arange(n_treatments) + 0.04,\n", + " direct_point_est,\n", + " yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\n", + " fmt=\"o\",\n", + " label=\"Direct Model\",\n", + ")\n", + "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", + "plt.ylabel(\"Effect\")\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of treatment effect from different models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could see the **adjusted surrogate index** approach does a good job overcomes a common data limitation when considering long-term effects of novel treatments and expands the surrogate approach to consider a common, and previously\n", + "problematic, pattern of serially correlated treatments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extensions -- Including Heterogeneity in Effect\n", + "\n", + "Finally, I will show that our EconML's `DynamicDML` and `LinearDML` estimators could not only learn Average Treatment Effect (ATE), but also **Heterogeneous Treatment Effect (CATE)**, which will return the treatment effect as a function of interested characteristics. In the example below, I will use first control variable as feature to learn effect heterogeneity, and retrain the final `LinearDML` model. Similarly, you could train `DynamicDML` with feature $X$ as well." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", + "Average treatment effect for each investment: [0.82738185 0.71610965 2.56087599]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031
X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098
X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856
cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779
cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "===========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-----------------------------------------------------------\n", + "X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031\n", + "X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098\n", + "X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224\n", + " CATE Intercept Results \n", + "=======================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-----------------------------------------------------------------------\n", + "cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856\n", + "cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779\n", + "cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024\n", + "-----------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 16;\n", + " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(\\n sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\\n)\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nprint(\\n \\\"Average treatment effect for each investment: \\\",\\n adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\\n)\\nadjsurr_est.summary(alpha=0.05)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# learn treatment effect on surrogate index on new dataset\n", + "from econml.dml import LinearDML\n", + "\n", + "adjsurr_est = LinearDML(\n", + " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", + ")\n", + "# fit treatment_0 on total revenue from new dataset\n", + "adjsurr_est.fit(\n", + " sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\n", + ")\n", + "# print treatment effect summary\n", + "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", + "print(\n", + " \"Average treatment effect for each investment: \",\n", + " adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\n", + ")\n", + "adjsurr_est.summary(alpha=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the summary table above, none of the coefficient for feature $X0$ is significant, that means there is no effect heterogeneity identified, which is consistent with the data generation process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusions\n", + "\n", + "In this notebook, we have demonstrated the power of using EconML to:\n", + "\n", + "* estimate treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes\n", + "* correct the bias coming from auto-correlation of the historical treatment policy\n", + "* use Machine Learning to enable estimation with high-dimensional surrogates and controls\n", + "* solve a complex problem using an unified pipeline with only a few lines of code\n", + "\n", + "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } }, - "pipeline.PNG": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "Microsoft provides multiple montetary and resource investments to enterprice customers in support of products adoption, the sales manager would like to know which of these programs (\"investments\") are more successful than others? Specifically, we are interested in identifying the average treatment effect of each investment at some period $t$, on the cumulative outcome in the subsequent $m$ months. \n", - "\n", - "There are a few challenges to answer this question. First of all, we haven't fully observed the long-term revenue yet and we don't want to wait that long to evaluate a program. In addition, a careful causal modeling is required to correctly attribute the long-term ROI of multiple programs in a holistic manner, avoiding the biased estimate coming from confounding effect or double counting issues. \n", - "\n", - "The causal graph below shows how to frame this problem:\n", - "\n", - "![causal_graph.PNG](attachment:causal_graph.PNG)\n", - "\n", - "**Methodology:** Our proposed adjusted surrogate index approach could address all the chanllenges above by assuming the long-term effect is channeled through some short-term observed surrogates and employing a dynamic adjustment step (`DynamicDML`) to the surrogate model in order to get rid of the effect from future investment, finally applying double machine learning (`DML`) techniques to estimate the ROI. \n", - "\n", - "The pipeline below tells you how to solve this problem step by step:\n", - "![pipeline.PNG](attachment:pipeline.PNG)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "from econml.data.dynamic_panel_dgp import SemiSynthetic\n", - "from sklearn.linear_model import LassoCV, MultiTaskLassoCV\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data\n", - "\n", - "The **semi-synthetic data*** is comprised of 4 components:\n", - " * **Surrogates:** short-term metrics that could represent long-term revenue\n", - " * **Treatments:** different types of monetary investments to the end customers\n", - " * **Outcomes:** cumulative long-term revenue\n", - " * **Controls:** lagged surrogates and treatments, other time-invariant controls (e.g. demographics)\n", - "\n", - "To build the semi-synthetic data we estimate a series of moments from a real-world dataset: a full covariance matrix of\n", - "all surrogates, treatments, and controls in one period and a series of linear prediction models (lassoCV) of each surrogate and\n", - "treatment on a set of 6 lags of each treatment, 6 lags of each surrogate, and time-invariant controls. Using these values, we draw new parameters from distributions matching the key characteristics of each family of parameters. Finally, we use these new\n", - "parameters to simulate surrogates, treatments, and controls by drawing a set of initial values from the covariance matrix and\n", - "forward simulating to match intertemporal relationships from the transformed prediction models. We use one surrogate to be the outcome of interests. Then we consider the effect of each treatment in period $t$ on the cumulative sum of outcome from following 4 periods. We can calculate the true treatment effects in the semi-synthetic data as a function of parameters from the linear prediction models.\n", - "\n", - "The input data is in a **panel format**. Each panel corresponds to one company and the different rows in a panel correspond to different time period. \n", - "\n", - "Example:\n", - "\n", - "||Company|Year|Features|Controls/Surrogates|T1|T2|T3|AdjRev|\n", - "|---|---|---|---|---|---|---|---|---|\n", - "|1|A|2018|...|...|\\$1,000|...|...|\\$10,000|\n", - "|2|A|2019|...|...|\\$2,000|...|...|\\$12,000|\n", - "|3|A|2020|...|...|\\$3,000|...|...|\\$15,000|\n", - "|4|A|2021|...|...|\\$3,000|...|...|\\$18,000|\n", - "|5|B|2018|...|...|\\$0|...|...|\\$5,000|\n", - "|6|B|2019|...|...|\\$1,000|...|...|\\$10,000|\n", - "|7|B|2020|...|...|\\$0|...|...|\\$7,000|\n", - "|8|B|2021|...|...|\\$1,200|...|...|\\$12,000|\n", - "|9|C|2018|...|...|\\$1,000|...|...|\\$20,000|\n", - "|10|C|2019|...|...|\\$1,500|...|...|\\$25,000|\n", - "|11|C|2020|...|...|\\$500|...|...|\\$18,000|\n", - "|12|C|2021|...|...|\\$500|...|...|\\$20,000|\n", - " \n", - " **For confidentiality reason, the data used in this case study is synthetically generated and the feature distributions don't exactly correspond to real distributions.*" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# generate historical dataset (training purpose)\n", - "np.random.seed(43)\n", - "dgp = SemiSynthetic()\n", - "dgp.create_instance()\n", - "n_periods = 4\n", - "n_units = 5000\n", - "n_treatments = dgp.n_treatments\n", - "random_seed = 43\n", - "thetas = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", - "\n", - "panelX, panelT, panelY, panelGroups, true_effect = dgp.gen_data(\n", - " n_units, n_periods, thetas, random_seed\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Outcome shape: (5000, 4)\n", - "Treatment shape: (5000, 4, 3)\n", - "Controls shape: (5000, 4, 71)\n" - ] - } - ], - "source": [ - "# print panel data shape\n", - "print(\"Outcome shape: \", panelY.shape)\n", - "print(\"Treatment shape: \", panelT.shape)\n", - "print(\"Controls shape: \", panelX.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# generate new dataset (testing purpose)\n", - "thetas_new = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", - "panelXnew, panelTnew, panelYnew, panelGroupsnew, true_effect_new = dgp.gen_data(\n", - " n_units, n_periods, thetas_new, random_seed\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n" - ] - } - ], - "source": [ - "# print true long term effect\n", - "true_longterm_effect = np.sum(true_effect_new, axis=0)\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Do Dynamic Adjustment with EconML\n", - "From the causal graph above, we could see we want to first remove the effects of future incentives from the historical outcomes to create an **adjusted long-term revenue** as if those future incentives never happened.\n", - "\n", - "EconML's `DynamicDML` estimator is an extension of Double Machine Learning approach to **dynamically estimate the period effect of treatments assigned sequentially over time period**. In this scenario, it could help us to adjust the cumulative revenue by subtracting the period effect of all of the investments after the target investment.\n", - "\n", - "For more details about `DynamicDML`, please read this [paper](https://arxiv.org/pdf/2002.07285.pdf). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper function to reshape the panel data\n", - "def long(x): # reshape the panel data to (n_units * n_periods, -1)\n", - " n_units = x.shape[0]\n", - " n_periods = x.shape[1]\n", - " return (\n", - " x.reshape(n_units * n_periods)\n", - " if np.ndim(x) == 2\n", - " else x.reshape(n_units * n_periods, -1)\n", - " )\n", - "\n", - "\n", - "def wide(x): # reshape the panel data to (n_units, n_periods * d_x)\n", - " n_units = x.shape[0]\n", - " return x.reshape(n_units, -1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", - " [1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00],\n", - " [2.000e+00, 2.000e+00, 2.000e+00, 2.000e+00],\n", - " ...,\n", - " [4.997e+03, 4.997e+03, 4.997e+03, 4.997e+03],\n", - " [4.998e+03, 4.998e+03, 4.998e+03, 4.998e+03],\n", - " [4.999e+03, 4.999e+03, 4.999e+03, 4.999e+03]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panelGroups" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.35952949 1.92451605 0.34684417]\n", - "[0.74662029 1.13138969 0.25069193 1.30585143 1.79051531 0.34597602]\n", - "[0.46734394 0.74952179 0.16292026 0.67056612 0.92299133 0.23686006\n", - " 1.36311063 1.91659314 0.34728767]\n" - ] - } - ], - "source": [ - "# on historical data construct adjusted outcomes\n", - "from econml.dynamic.dml import DynamicDML\n", - "\n", - "panelYadj = panelY.copy()\n", - "\n", - "est = DynamicDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=2\n", - ")\n", - "for t in range(1, n_periods): # for each target period 1...m\n", - " # learn period effect for each period treatment on target period t\n", - " est.fit(\n", - " long(panelY[:, 1 : t + 1]),\n", - " long(panelT[:, 1 : t + 1, :]), # reshape data to long format\n", - " X=None,\n", - " W=long(panelX[:, 1 : t + 1, :]),\n", - " groups=long(panelGroups[:, 1 : t + 1]),\n", - " )\n", - " print(est.intercept_)\n", - " # remove effect of observed treatments\n", - " T1 = wide(panelT[:, 1 : t + 1, :])\n", - " panelYadj[:, t] = panelY[:, t] - est.effect(\n", - " T0=np.zeros_like(T1), T1=T1\n", - " ) # reshape data to wide format" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train Surrogate Index\n", - "Once we have the adjusted outcome, we'd like to train any ML model to learn the relationship between short-term surrogates and long-term revenue from the historical dataset, assuming the treatment effect of investments on long-term revenue could **only** go through short-term surrogates, and the **relationship keeps the same** between the historical dataset and the new dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_formatted_code = \"# train surrogate index on historical dataset\\nXS = np.hstack(\\n [panelX[:, 1], panelYadj[:, :1]]\\n) # concatenate controls and surrogates from historical dataset\\nTotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\\nadjusted_proxy_model = LassoCV().fit(\\n XS, TotalYadj\\n) # train proxy model from historical dataset\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# train surrogate index on historical dataset\n", - "XS = np.hstack(\n", - " [panelX[:, 1], panelYadj[:, :1]]\n", - ") # concatenate controls and surrogates from historical dataset\n", - "TotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\n", - "adjusted_proxy_model = LassoCV().fit(\n", - " XS, TotalYadj\n", - ") # train proxy model from historical dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_formatted_code = \"# predict new long term revenue\\nXSnew = np.hstack(\\n [panelXnew[:, 1], panelYnew[:, :1]]\\n) # concatenate controls and surrogates from new dataset\\nsindex_adj = adjusted_proxy_model.predict(XSnew)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# predict new long term revenue\n", - "XSnew = np.hstack(\n", - " [panelXnew[:, 1], panelYnew[:, :1]]\n", - ") # concatenate controls and surrogates from new dataset\n", - "sindex_adj = adjusted_proxy_model.predict(XSnew)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run DML to Learn ROI with EconML\n", - "Finally we will call `LinearDML` estimator from EconML to learn the treatment effect of multiple investments on the adjusted surrogate index in new dataset. `LinearDML` is a two stage machine learning models for estimating **(heterogeneous) treatment effects** when all potential confounders are observed, it leverages the machine learning power to deal with **high dimensional dataset** and still be able to construct **confidence intervals**. \n", - "\n", - "For more details, please read this [paper](https://arxiv.org/pdf/1608.00060.pdf). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858
cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733
cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858\n", - "cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733\n", - "cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nadjsurr_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on surrogate index on new dataset\n", - "from econml.dml import LinearDML\n", - "\n", - "adjsurr_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "adjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "adjsurr_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 12;\n", - " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\nadjsurr_point_est = adjsurr_est.intercept_\\nadjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# save the treatment effect and confidence interval\n", - "adjsurr_point_est = adjsurr_est.intercept_\n", - "adjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Evaluation\n", - "Now we want to compare the proposed **adjusted surrogate index** approach with estimation from realized long-term outcome. Below we train another `LinearDML` model on the realized cumulative revenue directly, without any adjustment. And then we visualize the two models output, comparing with the ground truth." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304
cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004
cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304\n", - "cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004\n", - "cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 13;\n", - " var nbb_formatted_code = \"# learn treatment effect on direct outcome\\nfrom econml.dml import LinearDML\\n\\ndirect_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\ndirect_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\ndirect_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on direct outcome\n", - "from econml.dml import LinearDML\n", - "\n", - "direct_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "direct_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "direct_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 14;\n", - " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\ndirect_point_est = direct_est.intercept_\\ndirect_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# save the treatment effect and confidence interval\n", - "direct_point_est = direct_est.intercept_\n", - "direct_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Error bar plot of treatment effect from different models')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_formatted_code = \"# plot the error bar plot of different models\\nplt.figure(figsize=(18, 6))\\nplt.subplot(1, 2, 1)\\n\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\n\\nplt.subplot(1, 2, 2)\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments) + 0.04,\\n direct_point_est,\\n yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\\n fmt=\\\"o\\\",\\n label=\\\"Direct Model\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\nplt.suptitle(\\\"Error bar plot of treatment effect from different models\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot the error bar plot of different models\n", - "plt.figure(figsize=(18, 6))\n", - "plt.subplot(1, 2, 1)\n", - "\n", - "plt.errorbar(\n", - " np.arange(n_treatments) - 0.04,\n", - " true_longterm_effect,\n", - " fmt=\"o\",\n", - " alpha=0.6,\n", - " label=\"Ground truth\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments),\n", - " adjsurr_point_est,\n", - " yerr=(\n", - " adjsurr_conf_int_ub - adjsurr_point_est,\n", - " adjsurr_point_est - adjsurr_conf_int_lb,\n", - " ),\n", - " fmt=\"o\",\n", - " label=\"Adjusted Surrogate Index\",\n", - ")\n", - "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", - "plt.ylabel(\"Effect\")\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.errorbar(\n", - " np.arange(n_treatments) - 0.04,\n", - " true_longterm_effect,\n", - " fmt=\"o\",\n", - " alpha=0.6,\n", - " label=\"Ground truth\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments),\n", - " adjsurr_point_est,\n", - " yerr=(\n", - " adjsurr_conf_int_ub - adjsurr_point_est,\n", - " adjsurr_point_est - adjsurr_conf_int_lb,\n", - " ),\n", - " fmt=\"o\",\n", - " label=\"Adjusted Surrogate Index\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments) + 0.04,\n", - " direct_point_est,\n", - " yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\n", - " fmt=\"o\",\n", - " label=\"Direct Model\",\n", - ")\n", - "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", - "plt.ylabel(\"Effect\")\n", - "plt.legend()\n", - "plt.suptitle(\"Error bar plot of treatment effect from different models\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could see the **adjusted surrogate index** approach does a good job overcomes a common data limitation when considering long-term effects of novel treatments and expands the surrogate approach to consider a common, and previously\n", - "problematic, pattern of serially correlated treatments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extensions -- Including Heterogeneity in Effect\n", - "\n", - "Finally, I will show that our EconML's `DynamicDML` and `LinearDML` estimators could not only learn Average Treatment Effect (ATE), but also **Heterogeneous Treatment Effect (CATE)**, which will return the treatment effect as a function of interested characteristics. In the example below, I will use first control variable as feature to learn effect heterogeneity, and retrain the final `LinearDML` model. Similarly, you could train `DynamicDML` with feature $X$ as well." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Average treatment effect for each investment: [0.82738185 0.71610965 2.56087599]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031
X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098
X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856
cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779
cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------\n", - "X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031\n", - "X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098\n", - "X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856\n", - "cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779\n", - "cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(\\n sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\\n)\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nprint(\\n \\\"Average treatment effect for each investment: \\\",\\n adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\\n)\\nadjsurr_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on surrogate index on new dataset\n", - "from econml.dml import LinearDML\n", - "\n", - "adjsurr_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "adjsurr_est.fit(\n", - " sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\n", - ")\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "print(\n", - " \"Average treatment effect for each investment: \",\n", - " adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\n", - ")\n", - "adjsurr_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary table above, none of the coefficient for feature $X0$ is significant, that means there is no effect heterogeneity identified, which is consistent with the data generation process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusions\n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* estimate treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes\n", - "* correct the bias coming from auto-correlation of the historical treatment policy\n", - "* use Machine Learning to enable estimation with high-dimensional surrogates and controls\n", - "* solve a complex problem using an unified pipeline with only a few lines of code\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/Dynamic Double Machine Learning Examples.ipynb b/notebooks/Dynamic Double Machine Learning Examples.ipynb index d0fdebfb0..391f491ba 100755 --- a/notebooks/Dynamic Double Machine Learning Examples.ipynb +++ b/notebooks/Dynamic Double Machine Learning Examples.ipynb @@ -94,7 +94,7 @@ "outputs": [], "source": [ "# Main imports\n", - "from econml.dynamic.dml import DynamicDML\n", + "from econml.panel.dml import DynamicDML\n", "from econml.tests.dgp import DynamicPanelDGP, add_vlines\n", "\n", "# Helper imports\n", @@ -188,7 +188,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -461,7 +461,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17,