Textbooks and papers¶
During the first two weeks, our main textbook for theoretical reading will be “Introduction to Statistical Learning” by James, Witten, Hastie, and Tibshirani, which you can download for free.
For the last two weeks, there is no textbook available, as the material we learn is at the frontier of research. We will study based on the papers by the “stars” in the field of applying Machine Learning toolkit for Causal Inference:
High-dimensional methods and inference on structural and treatment effects by Alexandre Belloni, Victor Chernozhukov, and Christian Hansen (JEP, 2014)
Double/debiased machine learning for treatment and structural parameters by Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey (Econom. J., 2019)
Recursive partitioning for heterogeneous causal effects by Susan Athey and Guido Imbens (PNAS, 2016).
Generalized Random Forests by Susan Athey, Julie Tibshirani, and Stefan Wager (Annals of Statistics, 2019)