國立政治大學統計學系
學 術 演 講
主講人：黃名鉞助研究員 (中央研究院統計科學研究所)學 術 演 講
題 目：Robust Inference of Conditional Average Treatment Effects Using
Dimension Reduction
時 間：民國110年11月8日 (星期一) 下午1：30
地 點：國立政治大學逸仙樓050101教室
摘 要：
In this talk, I will propose a dimension reduction method to estimate the conditional average treatment effects based on observational data with multivariate or high-dimensional confounders. This method can reduce the curse of dimensionality as much as possible while keeping the nonparametric merit. To impute potential outcomes in a more stable way, a nonparametric regression with prior dimension reduction is further used. This procedure leads to better estimates than existing methods in finite sample, such as naive matching method and inverse propensity score weighting, and we demonstrated this phenomenon in our simulation studies. Further, we showed that the asymptotic variance of estimated central mean subspace is not involved in the asymptotic distribution of estimated conditional average treatment effects. According to this finding, we propose a more effective bootstrapping procedure without bootstrapping the estimated central mean subspace to estimate the asymptotic variances and make valid inference.
