Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs
重复截面设计中的非参数异质性处理效应估计
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…