Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
This work addresses a major challenge in estimating treatment effects in observational studies, which is crucial for researchers and practitioners working with heterogeneous data from multiple sources.
The authors tackled the problem of unmeasured confounding in observational studies and proposed an algorithm that can falsify the assumption of no unmeasured confounding, detecting dependencies with high statistical power. The algorithm was able to efficiently detect confounding on both simulated and semi-synthetic data.
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 semi-synthetic data.