Notebooks for Applied Causal Inference Powered by ML and AI

Colab notebooks exploring different Machine Learning topics.

Causal Inference Crash Course for Scientists - contains slides and Jupyter notebooks

Notebooks of Python and R code which illustrates basic causal inference using simulated data

R notebooks associated with the review article: "Causal inference methods for combining randomized controlled trials and observational studies: a review"

Notebooks for a short course on urban econometrics, also covering causal inference methods such as experiments, matching, difference in difference, instrumental variables, etc.

python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper

Notebooks (mostly R but some PyMC3) covering Prof Richard McElreath's Statistical Rethinking 2 book (draft version up to 26th Sept 2019) and Homeworks from his winter 2019 lecture course

Causal analysis simulations

R notebooks associated with the review article: "Generalizing a causal effect: sensitivity analysis and missing covariates"

Notebooks supporting my PyData Global 2024 talk

The main objectif is to conduct an exploratory data analysis on the data & communicate useful insights.

Explore the impact of discounts and tech support on revenue through Causal ML models. This repo provides an analysis notebook, data, and a guide on leveraging machine learning for strategic business decisions.

Impact of Referral Program on Revenue | Python, CausalML, DoWhy, EconML, Jupyter Notebook: Applied S-, T-, & X-learner causal models to quantify the referral program's impact, revealing a +$59.74 increase in order value (p < 0.001) and significantly boosting e-commerce revenue

Causality is a fundamental concept that seeks to determine the relationships between events, particularly to discern whether one event is the result of another. In this notebook we will establish causality by determining whether a change in one variable causes a change in another variable.