A Meta-learner for Heterogeneous Effects in Difference-in-Differences
This work addresses the challenge of flexible and robust estimation of treatment effects for researchers in econometrics and causal inference, representing an incremental improvement with extensions to broader settings.
The paper tackles the problem of estimating heterogeneous treatment effects in panel data using the Difference-in-Differences framework, proposing a novel doubly robust meta-learner for the Conditional Average Treatment Effect on the Treated that reduces estimation to convex risk minimization and demonstrates superiority over existing baselines in empirical results.
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.