  In causal inference, the Inverse Probability Weighting (IPW) estimator is
commonly used to estimate causal effects for estimands within the class of
Weighted Average Treatment Effect (WATE). When constructing confidence
intervals (CIs), robust sandwich variance estimators are frequently used for
practical reasons. Although these estimators are easy to calculate using
widely-used statistical software, they often yield narrow CIs for commonly
applied estimands, such as the Average Treatment Effect on the Treated and the
Average Treatment Effect for the Overlap Populations. In this manuscript, we
reexamine the asymptotic variance of the IPW estimator and clarify the
conditions under which CIs derived from the sandwich variance estimator are
conservative. Additionally, we propose new criteria to assess the
conservativeness of CIs. The results of this investigation are validated
through simulation experiments and real data analysis.
