學 術 演 講
學 術 演 講
題 目：Robust Inference of Conditional Average Treatment Effects Using
時 間：民國110年11月8日 (星期一) 下午1：30
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.