causalml-basics

This repository contains a collection of Jupyter notebooks focused on machine learning methods for causal inference. Each notebook provides applied examples using simulated and real data.

The notebooks are organized as follows:

1. OLS and Overfitting: This notebook shows the basic functionalities of statsmodels and pyfixest for linear regression with the case of overfitting.
2. Regression with Lasso: The goal is to predict wages using penalized linear regression.
3. Classification with Classic ML: This notebook introduces several classification methods, including Logit, LDA, and SVM.
4. Clustering: This notebooks explores dimensionality reduction techniques like Principal Component Analysis (PCA) and K-means clustering.
5. Tree-based Methods: Basic introduction to tree-based methods like Decision Trees and Random Forest.
6. Neural Networks: Basic introduction to neural networks using sklearn and pytorch.
7. Regression using ML: This notebook is based on a lab from Chapter 9 of the book Causal ML. The goal is to predict wages using non-linear models and stacking.
8. Double/Debiased ML: IN PROGRESS
9. Heterogeneous Treatment Effects: IN PROGRESS