Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs

Abstract

Identifying heterogeneity in a population's response to a health or policy intervention is crucial for evaluating and informing policy decisions. We propose a novel heterogeneous treatment effect estimator in the difference-in-differences design with repeated cross sectional data, where we observe different samples of a population at two time periods separated by the onset of a policy intervention, as well as samples of a population that serves as the control. Our estimator has orthogonality properties that enable fast rates on learning the treatment effect while allowing slower rates for estimating nuisance components. Our proposal shows promising empirical performance across a variety of simulation setups.

References in corpus (5)

• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Balanced Policy Evaluation and Learning
• Selective inference for effect modification via the lasso
• Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding
• Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation

Cited by in corpus (1)

• Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding