index.Rmd

--- 
title: "Machine Learning and Causal Inference"
author: "[Alexander Quispe](https://alexanderquispe.github.io/) & [Anzony Quispe](https://anzonyquispe.github.io/scientific/science.html)"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
---
# Preface
This bookdown has been created based on the tutorials of the course **14.388 Inference on Causal and Structural Parameters Using ML and AI** in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. [Jannis Kueck](https://www.kaggle.com/janniskueck/code) and 
[V. Chernozukhov](https://www.kaggle.com/victorchernozhukov/code) have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey's Machine Learning and Causal Inference [course](https://bookdown.org/connect/#/apps/3e3ee3cb-b53e-4956-b8d3-a3243e663162/access/1618). We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.

The main topics this book covers are: 

* Prediction/Inference with High Dimensional Linear Models
* Prediction in Modern Nonlinear Regressions (Random Forest and Deep Neural Networks)
* Randomized Control Trials
* Causal DAGs
* Double/debiased Machine Learning
* Heterogeneous Treatment Effects using Causal Trees
* Heterogeneous Treatment Effects using Causal Forest
* Feature Engineering With Deep Learning for Causal and Predictive Inference