[Statlist] ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Yuhao Wang, Tsinghua University

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-​Dimensional Confounders“  
by Yuhao Wang, Tsinghua University 

Time: Friday, 11 June 2021, 15.00 - 16.00
Place: Zoom at  https://ethz.zoom.us/j/97914933906

Abstract: We consider estimation of average treatment effects given observational data with high-​dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, 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. Our theoretical results quantify the price to pay for permitting the regression functions to be unestimable, which shows up as an inflation of the variance of the estimator compared to the semiparametric efficient variance by at most O(1) under mild conditions. 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, and so for example, the variances of the potential outcomes may be estimated. We show how confidence intervals centred on our estimates may be constructed, and also discuss an extension of the method to estimating projections of the heterogeneous treatment effect function.

M. Azadkia, Y. Chen, G. Chinot, M. Löffler, A. Taeb

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html

Young Data Science Researcher Seminar Zurich – Seminar for Statistics | ETH Zurich
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