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Activity Number: 460 - Causal Methods for Discovery, Confirmation and Mechanistic Evaluation
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309841
Title: On Variance of the Average Treatment Effect in the Treated When Using Inverse Probability Weighting
Author(s): Sarah Reifeis* and Michael Hudgens
Companies: University of North Carolina at Chapel Hill and UNC
Keywords: estimating equations; exposure effect; Huber-White sandwich estimator; observational data; variance estimation

In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment effect in the treated (ATT). The variance of the IPW ATE estimator is often estimated by treating the weights as known and then using the so-called ``robust'' (Huber-White) sandwich estimator, which results in conservative standard error (SE) estimation. Here it is shown that using such an approach when estimating the variance of the IPW ATT estimator does not necessarily result in conservative SE estimates. That is, treating the weights as known, the robust sandwich estimator may be conservative or anti-conservative. Thus confidence intervals and hypothesis tests of the ATT using the robust SE estimate will not be valid in general. Instead, stacked estimating equations which account for the weight estimation can be used to compute a consistent, closed-form variance estimator for the IPW ATT estimator. The two variance estimators are compared via simulation studies and in a data analysis of the effect of smoking on gene expression.

Authors who are presenting talks have a * after their name.

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