标题： 针对鲁棒ATE估计的结果感知加权方法 显示英文标题

标题： Outcome-Informed Weighting for Robust ATE Estimation

摘要： 从观测数据中可靠地估计因果效应需要调整混杂因素，并确保治疗组之间协变量分布有足够的重叠。然而，在高维设置下，缺乏重叠通常会增加方差并削弱基于逆倾向评分加权（IPW）的估计器的鲁棒性。尽管已经提出了许多依赖协变量调整的方法来缓解这些问题，但我们转而关注结果空间。本文引入了增强边际结果密度比（AMR）估计器，这是一种以结果为导向的加权方法，能够自然过滤无关信息，减轻实际正性违反问题，并在效率和鲁棒性方面优于标准增强IPW和基于协变量调整的方法。此外，通过消除对强先验假设的需求，我们的事后校准框架在具有高维协变量的情况下同样有效。我们展示了合成数据、NHANES 数据集以及文本应用上的实验结果，证明了AMR的鲁棒性和在弱重叠和高维协变量情况下的优越性能。 显示英文摘要

摘要： Reliable causal effect estimation from observational data requires adjustment for confounding and sufficient overlap in covariate distributions between treatment groups. However, in high-dimensional settings, lack of overlap often inflates the variance and weakens the robustness of inverse propensity score weighting (IPW) based estimators. Although many approaches that rely on covariate adjustment have been proposed to mitigate these issues, we instead shift the focus to the outcome space. In this paper, we introduce the Augmented Marginal outcome density Ratio (AMR) estimator, an outcome-informed weighting method that naturally filters out irrelevant information, alleviates practical positivity violations and outperforms standard augmented IPW and covariate adjustment-based methods in terms of both efficiency and robustness. Additionally, by eliminating the need for strong a priori assumptions, our post-hoc calibration framework is also effective in settings with high-dimensional covariates. We present experimental results on synthetic data, the NHANES dataset and text applications, demonstrating the robustness of AMR and its superior performance under weak overlap and high-dimensional covariates.