A1293
Title: Nonparametric Uniform Inference for General Heterogeneous Treatment Effect Models and Measurement Errors
Authors: Wei Huang - University of Melbourne (Australia) [presenting]
Zheng Zhang - Renmin University of China (China)
Haoze Hou - Renmin University of China (China)
Chunrong Ai - University of Florida (United States)
Abstract: Estimating heterogeneous treatment effect plays a central role in economics, social science and psychology, among others. Existing literature focuses on studying the conditional average treatment effect (CATE), assuming all variables are measured without errors. A unified framework is proposed for estimating general heterogeneous treatment effects (GHTE), including when the conditioning covariates may be exposed to classical measurement errors. The framework includes conditional average, quantile and asymmetric least squares treatment effects as special cases. Under the unconfoundedness condition, we derive the functional limit theory for the proposed estimators and provide an easy-to-implement procedure for uniform inference based on the multiplier bootstrap.
