"Part I

The first part of the course will cover Machine Learning in Econometrics. The package R will be used throughout to demonstrate the techniques. The course will provide a practical introduction to modern high-dimensional function fitting methods — a.k.a. machine learning (ML) methods — for efficient estimation and inference on the treatment effects and structural parameters in empirical economic models. Participants will use R to immediately internalize and use the techniques in their own academic and industry work. All lectures, except the introductory ones, will be accompanied by the R-code that can be used to reproduce the empirical examples in the lectures during the lectures. Thus, there will be no gap between theory and practice.

Outline: Review of classical regression for prediction and causal inference; Causal inference in approximately sparse linear structural equations models; Understanding of the inference strategy via the double partialling out and adaptivity; ML methods for prediction (reduced form estimation and evaluation of ML methods using test samples); ML methods for causal parameters, double ML for causal parameters in treat effect models and non-linear econometric models.

Part II

Part II

Will review some recent developments in unsupervised learning and causal machine learning with panel data. The focus is on recent advances about factor model, clustering, and text analysis.

Outline: Basics of unsupervised learning, causal inference/learning with panel data

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