A Meta-learner for Heterogeneous Effects in Difference-in-Differences

TL;DR

This paper introduces a robust meta-learner for estimating heterogeneous treatment effects in panel data using the Difference-in-Differences framework, leveraging machine learning for flexible and accurate effect estimation.

Contribution

It presents a novel doubly robust meta-learner for CATT that is flexible, robust to estimation errors, and extends to covariate shift and non-compliance scenarios.

Findings

Outperforms existing methods in empirical tests

Provides a flexible framework for heterogeneous effect estimation

Extends to covariate shift and instrumented DiD settings

Abstract

We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel trends assumption. We propose a novel doubly robust meta-learner for the Conditional Average Treatment Effect on the Treated (CATT), reducing the estimation to a convex risk minimization problem involving a set of auxiliary models. Our framework allows for the flexible estimation of the CATT, when conditioning on any subset of variables of interest using generic machine learning. Leveraging Neyman orthogonality, our proposed approach is robust to estimation errors in the auxiliary models. As a generalization to our main result, we develop a meta-learning approach for the estimation of general conditional functionals under covariate shift. We also provide an extension to the instrumented DiD setting with non-compliance. Empirical results demonstrate the superiority of our approach over existing baselines.