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# Yuhao WANG (Tsinghua University) – ” Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders “

## November 2, 2:00 pm - 3:15 pm

The Statistical Seminar: Every Monday at 2:00 pm.

Time: 2:00 pm – 3:15 pm
Date: 2nd of November 2020
Place: Visio

Yuhao WANG (Tsinghua University)Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders

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. 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 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. This is joint work with Rajen Shah from the University of Cambridge.

Organizers:
Cristina BUTUCEA (CREST), Alexandre TSYBAKOV (CREST), Karim LOUNICI (CMAP) , Zoltan SZABO (CMAP)