Machine Learning for Econometrics

Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques.

1 - Introduction
2 - Statistical tools
3 - Causal inference
4 - Post-selection inference
5 - Generalization and methodology
6 - High dimension and endogeneity
7 - Going further
8 - Inference on heterogeneous effects
9 - Optimal policy learning
10 - The synthetic control method
11 - Forecasting in high-dimension
12 - Working with text data
13 - Word embeddings
14 - Modern language models
15 - Exercises

Christophe Gaillac is an Associate Professor at the University of Geneva, GSEM. He was a postdoctoral prize research fellow at Oxford University and Nuffield College, and received his PhD in Economics from the Toulouse School of Economics. Jérémy L'Hour is a quantitative researcher at Capital Fund Management (CFM), a Paris-based systematic hedge fund. He received his PhD in Economics from Université Paris-Saclay.