具有未观察到的中介混杂因素的前门因果结构中的近端识别与估计
Proximal Identification and Estimation in Front-Door Causal Structures with Unobserved Confounding of the Mediator
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中文总结 AI 辅助
研究前门因果结构中未观察到的混杂因素问题，提出前门准则的近端推广，推导新识别策略，给出估计策略并通过模拟评估性能，拓展了前门准则适用性。
中文摘要 AI 辅助
未观察到的混杂因素是因果推断问题中的一个基本障碍。在图形建模文献中，已经发展出一种允许在存在隐藏变量的情况下进行识别的一般理论，但存在一些局限性。特别是，Pearl著名的前门准则允许在存在未观察到的处理和结果的共同原因的情况下进行非参数识别，然而它需要存在一个无混杂因素来介导从处理到结果的所有因果影响。这一严格要求限制了前门准则的适用性。我们提出了前门准则的近端推广，允许任意的处理/结果混杂以及中介的未观察到的混杂因素，前提是观察到后一种混杂因素的信息性代理变量。除了在这种情况下推导三种新的识别策略外，我们还为所得泛函提供了基于插件和影响函数的估计策略，并通过模拟评估它们的性能。
英文摘要
Unobserved confounding is a fundamental obstacle in causal inference problems. In the graphical modeling literature, a general theory has been developed that allows identification in the presence of hidden variables, with some limitations. In particular, Pearl's celebrated front-door criterion allows nonparametric identification in the presence of unobserved common causes of the treatment and the outcome, however it requires the presence of an unconfounded variable that mediates all causal influence from the treatment to the outcome. This stringent requirement limits the applicability of the front-door criterion. We propose proximal generalizations of the front-door criterion, allowing both arbitrary treatment/outcome confounding, and unobserved confounders of the mediator, provided informative proxies for the latter type of confounders are observed. In addition to deriving three new identification strategies in this setting, we provide plug-in and influence function-based estimation strategies for the resulting functionals, and evaluate their performance through simulations.
