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In causal inference, the Inverse Probability Weighting (IPW) estimator iscommonly used to estimate causal effects for estimands within the class ofWeighted Average Treatment Effect (WATE). When constructing confidenceintervals (CIs), robust sandwich variance estimators are frequently used forpractical reasons. Although these estimators are easy to calculate usingwidely-used statistical software, they often yield narrow CIs for commonlyapplied estimands, such as the Average Treatment Effect on the Treated and theAverage Treatment Effect for the Overlap Populations. In this manuscript, wereexamine the asymptotic variance of the IPW estimator and clarify theconditions under which CIs derived from the sandwich variance estimator areconservative. Additionally, we propose new criteria to assess theconservativeness of CIs. The results of this investigation are validatedthrough simulation experiments and real data analysis.

Evaluating the Conservativeness of Robust Sandwich Variance Estimator in Weighted Average Treatment Effects