提出新方法检测观测数据中隐藏混杂，无需随机实验。
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
- 通过检验不同环境下的因果机制是否独立来识别隐藏混杂
- 在模拟和半合成数据上高效检测到混杂现象
- 适用于环境直接影响结果的复杂场景，适合因果推断研究者
观察性研究中估计处理效应的一大挑战是依赖不可验证的假设，如无未测量混杂。本文提出一种算法，可在来自多个异质来源（称为环境）的观测数据中，检验并证伪无未测量混杂的假设。该方法基于关键观察：未测量混杂会导致可观测的因果机制之间出现依赖。我们构建了一个两阶段程序，在保持低误报率的同时，具有高统计功效来检测此类依赖。该算法无需随机数据，且在环境对结果有直接影响（即存在可迁移性违反）时仍有效。通过模拟与半合成数据验证，本方法能高效识别混杂，展现出良好的实用性。
原文摘要 · Abstract (English)
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.
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