These are the lecture notes for the course Machine Learning for Econometrics (High-Dimensional Econometrics, previously) taught in the third year of ENSAE Paris and the second year of the Master in Economics of Institut Polytechnique de Paris.
They cover 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 analysis of text data.
The focus will be on policy evaluation problems. Recent advances in causal inference such as the synthetic controls method will be reviewed.
They cover 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 analysis of text data.
The focus will be on policy evaluation problems. Recent advances in causal inference such as the synthetic controls method will be reviewed.