论文标题
关于通过Verma约束的前门模型的测试性
On Testability of the Front-Door Model via Verma Constraints
论文作者
论文摘要
尽管在治疗和结果之间存在未衡量的混杂因素，但前门标准可用于识别和计算因果关系。但是，主要假设 - （i）存在完全介导治疗对结果影响的变量（或一组变量）的存在，并且（ii）同时并不像治疗结果对一样遭受类似的混淆问题 - 通常被认为是令人难以置信的。本文探讨了这些假设的可检验性。我们表明，在涉及辅助变量的温和条件下，可以通过广义平等约束也可以测试前门模型中编码的假设（以及简单的扩展）。我们基于此观察结果提出了两项拟合测试，并评估我们对真实和合成数据的提议的功效。我们还将理论和经验比较与仪器变量方法来处理未衡量的混杂。
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.
