量化个体治疗反应的随机不确定性，提升因果推断的可靠性。
Estimating Aleatoric Uncertainty in the Causal Treatment Effect
- 引入治疗效应方差（VTE）和条件方差（CVTE）作为个体响应不确定性的度量。
- 在弱假设下可从观测数据中识别出这些不确定性，即使存在未观测混杂因素。
- 提出非参数核估计方法，实验证明性能优于或相当基线方法。
以往因果推断研究主要关注治疗效应的均值和条件均值，对个体治疗反应的变异性与不确定性关注较少。本文引入治疗效应方差（VTE）和条件治疗效应方差（CVTE）作为个体响应中固有的随机不确定性（aleatoric uncertainty）的自然度量，并证明在温和假设下，这些量可从观测数据中识别，即使存在未观测混杂因素。我们进一步提出了基于非参数核的VTE与CVTE估计器，理论分析表明其具有收敛性。通过在合成数据和半模拟数据集上的大量实验，验证了该方法性能优于或相当于朴素基线。
原文摘要 · Abstract (English)
Previous work on causal inference has primarily focused on averages and conditional averages of treatment effects, with significantly less attention on variability and uncertainty in individual treatment responses. In this paper, we introduce the variance of the treatment effect (VTE) and conditional variance of treatment effect (CVTE) as the natural measure of aleatoric uncertainty inherent in treatment responses, and we demonstrate that these quantities are identifiable from observed data under mild assumptions, even in the presence of unobserved confounders. We further propose nonparametric kernel-based estimators for VTE and CVTE, and our theoretical analysis establishes their convergence. We also test the performance of our method through extensive empirical experiments on both synthetic and semi-simulated datasets, where it demonstrates superior or comparable performance to naive baselines.
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