Machine Learning for Econometrics (previously "High-Dimensional Econometrics")
ENSAE Paris and Institut Polytechnique de Paris
joint with Christophe Gaillac
This course covers recent applications of high-dimensional statistics and machine learning to econometrics, including variable selection, inference with high-dimensional nuisance parameters in different settings, heterogeneity, treatment allocation, networks and text data. The focus will be on policy evaluation problems. Recent advances in causal inference such as the synthetic control method will be reviewed.
The goal of the course is to give insights about these new methods, their benefits and their limitations. It will mostly benefit students who are highly curious about recent advances in econometrics, whether they want to study theory or use them in applied work. Students are expected to be familiar with Econometrics 2 (2A) and Statistical Learning (3A).
In 2020, the outline was :
Introduction
High-Dimension, Variable Selection and Post-Selection Inference
Methodology: Using Machine Learning Tools in Econometrics
High-Dimension and Endogeneity
The Synthetic Control Method
Machine Learning Methods for Heterogeneous Treatment Effects
Prediction Policy Problems
Fairness and optimal treatment allocation
We decided to remove the link to the lecture notes, as they are outdated, and the book will be released very soon.
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Courses :
Machine Learning for Econometrics, short version (2023), The FiME Lab Summer School on Big Data and Finance, 12-16 June 2023, with. C. Gaillac
Machine Learning for Econometrics (2020, 2021), ENSAE Paris, with C. Gaillac and B. Crépon, then B. Crépon and A. Strittmatter
High-Dimensional Econometrics (2018, 2019), ENSAE Paris, with C. Gaillac
Econométrie en grande dimension (2020), Insee, github repository
"Synthetic control" in Seminar of Statistical Modeling (2020, 2021), ENSAE Paris, video
Chapter on the Synthetic Control Method in Microeconometric Evaluation of Public Policies (2016, 2017, 2018, 2019, 2020), ENSAE Paris, A. John and B. Crépon, then B. Crépon and A. Strittmatter
"NLP for product classification" in Quantitative Marketing Seminar (2021), ENSAE Paris
Probability (2018), Ecole Polytechnique, Master in Economics
Probability and Statistics (2017), Insee, with V. Cottet
TA sessions :
Measure Theory (2015), ENSAE Paris, A. Dalalyan
Econometrics (2016), ENSAE Paris, B. Crépon
Econometrics 1 (2016, 2017, 2018), ENSAE Paris, M. Visser
Econometrics 2 (2016, 2017, 2018, 2019), ENSAE Paris, X. D'Haultfoeuille
Mathematical Statistics 2 (2016, 2017, 2018, 2019), ENSAE Paris, A. Tsybakov
Project supervision :
"NLP for classifying scanner data", Applied statistics projects (2020, 2021), ENSAE Paris, with N. Chopin