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Title: Debiased inverse propensity score weighting for estimation of average treatment effects in high-dimensions Authors:  Rajen D Shah - University of Cambridge (United Kingdom) [presenting]
Yuhao Wang - Tsinghua University (China)
Abstract: Estimation of average treatment effects given observational data with high-dimensional pretreatment variables is considered. Existing methods for this problem typically assume some form of sparsity for the regression functions. We introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers $\sqrt{n}$ consistent estimates of the average treatment effect when the propensity score follows a sparse logistic regression model; the regression functions are permitted to be arbitrarily complex. Given the lack of assumptions on the regression functions, averages of transformed responses under each treatment may also be estimated at the $\sqrt{n}$ rate. So, for example, the variances of the responses may be estimated. We show how confidence intervals centred on our estimates may be constructed, and also extend the method to estimate heterogeneous treatment effects.