Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs

Understand

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.