基于Verma约束的前门模型可测性研究
On Testability of the Front-Door Model via Verma Constraints
摘要：
尽管治疗和结果之间存在不可测量的混杂因素，但前门标准可用于识别和计算因果效应。然而，关键假设-（i）存在一个变量（或一组变量），完全介导治疗对结果的影响，（ii）同时不存在与治疗-结果对类似的混淆问题-通常被认为是不可信的。本文探讨了这些假设的可检验性。我们表明，在温和的条件下，涉及一个辅助变量，编码的前门模型（和简单的扩展）的假设可以通过广义等式约束a.k.a Verma约束进行测试。我们提出了两个拟合优度测试的基础上，这一观察，并评估我们的建议的真实和合成数据的有效性。我们还提供了理论和实证比较工具变量的方法来处理不可测量的混杂。
英文摘要：
The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome. However, the key assumptions -- (i) the existence of a variable (or set of variables) that fully mediates the effect of the treatment on the outcome, and (ii) which simultaneously does not suffer from similar issues of confounding as the treatment-outcome pair -- are often deemed implausible. This paper explores the testability of these assumptions. We show that under mild conditions involving an auxiliary variable, the assumptions encoded in the front-door model (and simple extensions of it) may be tested via generalized equality constraints a.k.a Verma constraints. We propose two goodness-of-fit tests based on this observation, and evaluate the efficacy of our proposal on real and synthetic data. We also provide theoretical and empirical comparisons to instrumental variable approaches to handling unmeasured confounding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
