CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning

TL;DR

This paper reviews machine learning methods for estimating personalized treatment effects, demonstrating their application in real data and comparing their performance through simulations.

Contribution

It introduces a comprehensive toolbox of novel ML-based methods for estimating the conditional average treatment effect and applies them to empirical case studies.

Findings

Positive treatment effects found in both case studies

Conflicting evidence on treatment effect heterogeneity

Simulation results compare method performance

Abstract

For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow 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 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, 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 401(k) example, we find a positive treatment effect for all observations but conflicting 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.