Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms

2025-02-10

AI Summary

In observational studies, the untestable no-unmeasured-confounding assumption severely undermines causal inference reliability. To address this, we propose a counterfactual falsification framework leveraging heterogeneous data from multiple sources. Our method is the first to formalize “causal mechanism independence” as a testable implication of no unmeasured confounding. It models cross-environment dependencies among causal mechanisms and conducts a two-stage statistical test using high-power independence measures (e.g., HSIC or KCSD). Unlike randomized experiments, our approach requires no intervention and enables cross-environment failure detection. Extensive evaluations on synthetic and real-world datasets demonstrate that our method achieves significantly higher detection power for unmeasured confounding than existing falsification techniques, while maintaining strict control over false positive rates. This work establishes the first operational, empirically verifiable framework for confounding diagnosis in observational causal inference.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mechanisms to appear dependent. Building on this observation, we develop a novel two-stage procedure that detects these dependencies with high statistical power while controlling false positives. The algorithm does not require access to randomized data and, in contrast to other falsification approaches, functions even under transportability violations when the environment has a direct effect on the outcome of interest. To showcase the practical relevance of our approach, we show that our method is able to efficiently detect confounding on both simulated and real-world data.

Problem

• Detects unmeasured confounding in observational data
• Tests independence of causal mechanisms across environments
• Operates without randomized data or transportability assumptions

Innovation

• Testing causal mechanisms independence
• Detects unmeasured confounding dependencies
• Functions under transportability violations