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.

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