  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.
