Estimating Aleatoric Uncertainty in the Causal Treatment Effect

2026-02-09

AI Summary

This study addresses a critical limitation in existing causal inference methods, which predominantly focus on average treatment effects while neglecting the stochastic variability in individual responses. To capture this uncertainty, the paper introduces the variance of treatment effects (VTE) and the conditional variance of treatment effects (CVTE) as central measures of causal response heterogeneity. Under relatively weak assumptions allowing for unobserved confounding, the authors establish the identifiability of these variance measures and propose a consistent nonparametric kernel-based estimation framework. Theoretical analysis demonstrates the convergence properties of the proposed estimators, and experiments on synthetic and semi-simulated data show that the method achieves estimation accuracy that either matches or surpasses current baselines, thereby moving beyond the conventional mean-centric paradigm in causal inference.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Aleatoric Uncertainty
• Causal Treatment Effect
• Variance of Treatment Effect
• Individual Treatment Response
• Uncertainty Quantification

Innovation

• Aleatoric Uncertainty
• Causal Treatment Effect
• Variance of Treatment Effect
• Nonparametric Estimation
• Unobserved Confounders