Machine Learning for Econometrics

Objective

Eight sessions of three hours presenting important concepts and methods on machine learning for econometrics. The course focuses on flexible models and causal inference in high dimensions. Most of the sessions display a mix between theoretical considerations and practical application with hands-on in python or R.

The last session is a presentation of the course evaluation project by groups of students.

High dimension: sparsity in confounders (lasso, double lasso), nonlinearities in confounders (double ML), heterogeneities of effects (generic ML).

Planning

1. Session 1 – Statistical learning and regularized linear models

2. Session 2 – Flexible models for tabular data

3.  Potential outcomes, Directed Acyclic Graphs, confounder selection

4. Session 4a – Event studies: Causal methods for panel data

5. Session 4b – Double-lasso for statistical inference

6. Session 5 – Double machine learning: Neyman-orthogonality

7. Session 6 – Heterogeneous treatment effect

8. Session 7 – Heterogeneous treatment effect

References

Lecture notes are available online at Machine Learning for Econometrics — mleco 0.0.0 documentation

Loïc Estève, Guillaume Lemaitre, Olivier Grisel, Gael Varoquaux, Arturo Amor, Lilian, Benoit Rospars, Thomas Schmitt, Lucy Liu, Bruno P. Kinoshita, hackmd-deploy, ph4ge, Peter Steinbach, Alexandre Boucaud, Benson Muite, Jérémie du Boisberranger, Michael Notter, Pierre, Shane P, alagarrigue, Mehrdad Mohammadian, and parmentelat. Inria/scikit-learn-mooc: third mooc session. Zenodo, 2022. URL: https://doi.org/10.5281/zenodo.7220307, doi:10.5281/zenodo.7220307.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. 2017.

Kevin P Murphy. Probabilistic machine learning: an introduction. MIT press, 2022.

Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. Applied causal inference powered by ml and ai. arXiv preprint arXiv:2403.02467, 2024. URL: https://causalml-book.org.

Stefan Wager. Stats 361: causal inference. Stanford University, 2020. URL: https://web.stanford.edu/~swager/stats361.pdf.

Tyler J VanderWeele. Principles of confounder selection. European journal of epidemiology, 34:211–219, 2019.

C Gaillac and J L’Hour. Machine learning for econometrics, lecture notes ensae paris. Lecture notes, 2019.

Alberto Abadie. Using synthetic controls: feasibility, data requirements, and methodological aspects. Journal of economic literature, 59(2):391–425, 2021.

Stefan Wager and Susan Athey. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523):1228–1242, 2018.

Xinkun Nie and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2):299–319, 2021.

Toru Kitagawa and Aleksey Tetenov. Who should be treated? empirical welfare maximization methods for treatment choice. Econometrica, 86(2):591–616, 2018.