Content

The course is organized in 3 parts as follows:

1. Part 1: Introduction

     - Chapter 1: Introduction -> What is machine learning? Causal vs predictive problems and applications

2. Part 2: Machine learning for linear models

    - Chapter 2: The LASSO -> Learn what is high-dimensional data

    - Chapter 3: Inference with the LASSO -> Several applications of the LASSO in causal inference (e.g., returns to education, wage gap, growth, law and economics, wealth accumulation)

3. Part 3: Machine learning for nonlinear models

    - Chapter 4: Nonparametric methods -> Random forests, neural networks

    - Chapter 5: Causal inference with machine learning in nonparametric models

Course material

Slides, problem sets and R codes (made available after the tutorials)

List of useful sources/textbooks including:

- James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning: with applications in R (Vol. 112, p. 18). New York: Springer.

- Chernozhukov, Victor, Chris Hansen, Kallus Nathan, Martin Spindler and Skyrkanis, Vasilis. (2024): Applied Causal Inference Powered by ML and AI.