Assumption-Lean Differential Variance Inference for Heterogeneous Treatment Effect Detection

2025-12-02

AI Summary

Conventional conditional average treatment effect (CATE)-based methods struggle to identify treatment effect heterogeneity when effect modifiers are unobserved or subject to severe measurement error. Method: We propose a variance-comparison inference framework that does not require fully observed covariates. Leveraging the variance difference of potential outcomes as a novel causal identification anchor, we construct a doubly robust and asymptotically linear nonparametric estimator, integrating causal machine learning with a variance-sensitive testing paradigm. Contribution/Results: We establish theoretical consistency and asymptotic normality under weak regularity conditions. In a reanalysis of a randomized controlled trial, our method detects statistically significant heterogeneity in therapeutic hypothermia efficacy. The approach demonstrates robustness across diverse data-generating mechanisms and overcomes the strong reliance of CATE-based methods on high-fidelity covariate measurement.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

The conditional average treatment effect (CATE) is frequently estimated to refute the homogeneous treatment effect assumption. Under this assumption, all units making up the population under study experience identical benefit from a given treatment. Uncovering heterogeneous treatment effects through inference about the CATE, however, requires that covariates truly modifying the treatment effect be reliably collected at baseline. CATE-based techniques will necessarily fail to detect violations when effect modifiers are omitted from the data due to, for example, resource constraints. Severe measurement error has a similar impact. To address these limitations, we prove that the homogeneous treatment effect assumption can be gauged through inference about contrasts of the potential outcomes'variances. We derive causal machine learning estimators of these contrasts and study their asymptotic properties. We establish that these estimators are doubly robust and asymptotically linear under mild conditions, permitting formal hypothesis testing about the homogeneous treatment effect assumption even when effect modifiers are missing or mismeasured. Numerical experiments demonstrate that these estimators'asymptotic guarantees are approximately achieved in experimental and observational data alike. These inference procedures are then used to detect heterogeneous treatment effects in the re-analysis of randomized controlled trials investigating targeted temperature management in cardiac arrest patients.

Problem

• Detects heterogeneous treatment effects without effect modifiers
• Assesses homogeneous treatment effect assumption via outcome variances
• Provides robust inference despite missing or mismeasured covariates

Innovation

• Inference contrasts potential outcomes' variances
• Doubly robust causal machine learning estimators
• Detect heterogeneous effects with missing modifiers