Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 005

CATE Meets ML: Conditional Average Treatment Effect and Machine Learning

Daniel Jacob

For treatment effects - one of the core issues in modern econometric analysis -
prediction and estimation are flip-sides of the same coin. As it turns out,
machine learning methods are the tool for generalized prediction models.
Combined with econometric theory allows us to estimate not only the average but
a personalized treatment effect - the conditional average treatment effect
(CATE). In this tutorial, we give an overview of novel methods, explain them in
detail, and apply them via Quantlets in real data applications. We study the
effect that microcredit availability has on the amount of money borrowed and if
the 401(k) pension plan eligibility has an impact on net financial assets, as
two empirical examples. The presented toolbox of methods contains meta-
learners, like the Doubly-Robust, the R-, T- and X-learner, and methods that are
specially designed to estimate the CATE like the causal BART and the generalized
random forest. In both, the microcredit and the 401(k) example, we find a
positive treatment effect for all observations but diverse evidence of treatment
effect heterogeneity. An additional simulation study, where the true treatment
effect is known, allows us to compare the different methods and to observe
patterns and similarities.

Causal Inference, CATE, Machine Learning, Tutorial

JEL Classification: