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.

翻译：在因果推断中，逆概率加权（IPW）估计量常用于估计加权平均处理效应（WATE）类别的目标量。在构建置信区间（CI）时，出于实用考虑，常采用稳健三明治方差估计量。尽管这些估计量可通过广泛使用的统计软件轻松计算，但它们对于常见的目标量（如处理组平均处理效应和重叠人群平均处理效应）往往会产生较窄的置信区间。本文重新检验了IPW估计量的渐近方差，并阐明了基于三明治方差估计量得出的置信区间具有保守性的条件。此外，我们提出了评估置信区间保守性的新准则。本研究的结果通过模拟实验和真实数据分析得到了验证。