High-Dimensional Econometrics, ENSAE

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, networks and text data. The focus will be on policy evaluation problems. Recent advances in causal inference such as the synthetic controls 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). This is not a "machine learning for economists" course.

  1. Introduction
  2. High-dimension, model selection and post-selection inference
  3. High-dimensional methods for treatment effects
  4. Other advances in causal inference
  5. High-dimension and heterogeneity
  6. Econometrics of new kinds of data
  7. Optimal policy estimation, high-dimension and theory testing

(material coming soon)



High-Dimensional Econometrics (2018, 2019), ENSAE, with C. Gaillac

Probability (2018), Ecole Polytechnique, Master in Economics

Chapter on Synthetic Controls in Microeconometric Evaluation of Public Policies (2016, 2017, 2018), ENSAE, A. John and B. Crépon

Probability and Statistics (2017), Insee, with V. Cottet

TA sessions:

Measure Theory (2015), ENSAE, A. Dalalyan

Econometrics 2 (2016, 2017, 2018, 2019), ENSAE, X. D'Haultfoeuille

Mathematical Statistics 2 (2016, 2017, 2018, 2019), ENSAE, A. Tsybakov

Econometrics (2016), ENSAE, B. Crépon

Econometrics 1 (2016, 2017, 2018), ENSAE, M. Visser