We are pleased to announce the publication by Editions Economica (ENSAE Collection, Advanced Economics and Statistics) of Machine Learning for Econometrics by Christophe Gaillac and Jeremy L’hour.

Two PhD alumni from CREST

Christophe Gaillac is a Postdoctoral Prize Research Fellow in Economics at Nuffield College, University of Oxford and was previously a PhD student at CREST. His research focuses on Econometrics, Statistics, and Machine Learning. Christophe is also an alumni from École polytechnique and ENSAE Paris where he was, later, the coordinator of statistical and mathematical teachings from 2017 to 2019 at ENSAE Paris.

Jeremy L’hour works on econometric methods and applications of machine learning to causal inference. He currently works as a quantitative researcher in the statistical arbitrage alpha team at Capital Fund Management (CFM) and he holds a PhD in Economics from Université Paris-Saclay prepared at CREST-ENSAE. Jeremy is an alumni of ENSAE Paris and he was the coordinator for econometrics teachings between 2016 and 2019.

Machine Learning For Econometrics

“Machine Learning pour l’économétrie” is a work intended for economists who wish to grasp modern machine learning techniques – from their predictive performance to the revolutionary processing of unstructured data – in order to establish causality relationships from the data.

It addresses the automatic selection of variables in various high-dimensional contexts, the estimation of treatment effects 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. Each chapter contains a series of empirical examples, programs, and exercises to facilitate the adoption and implementation of techniques by the reader.

This book is aimed at master’s or graduate students, researchers, and practitioners eager to understand and enhance their knowledge of machine learning to apply it in a context traditionally reserved for econometrics.