基于前门模型的灵活非参数因果效应推断
Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
AI总结：
本文提出基于前门模型的非参数因果推断方法，开发了针对平均处理效应和处理后的平均处理效应的估计器，并通过模拟和实际数据验证了方法的有效性。
摘要：
在观察性研究中评估因果处理效应需要解决混杂问题。尽管后门准则可通过调整观测协变量来识别，但在存在未测量混杂时会失效。前门准则通过利用完全中介治疗效应的变量并避免未测量混杂因素的影响提供了替代方案。我们开发了新的一步和目标最小损失估计器，用于在前门假设下估计平均处理效应和处理后的平均处理效应。我们的估计器基于多个观测数据分布的参数化方法，包括完全避免建模中介密度的方法，并且与灵活的机器学习非参数估计兼容。我们通过推导二阶余项界限，建立了根n一致性条件和渐近线性条件。我们还开发了灵活的检验方法，用于评估识别假设，包括双重鲁棒检验程序，这在前门模型的半参数扩展中编码了广义（Verma）独立性约束。我们进一步展示了如何利用这些约束来提高因果效应估计器的效率。模拟研究证实了有限样本性能的优势，教育和急诊医学的实际数据应用展示了我们方法的实用性。
英文摘要：
Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed covariates, it fails in the presence of unmeasured confounding. The front-door criterion offers an alternative by leveraging variables that fully mediate the treatment effect and are unaffected by unmeasured confounders of the treatment-outcome pair. We develop novel one-step and targeted minimum loss-based estimators for both the average treatment effect and the average treatment effect on the treated under front-door assumptions. Our estimators are built on multiple parameterizations of the observed data distribution, including approaches that avoid modeling the mediator density entirely, and are compatible with flexible, machine learning-based nuisance estimation. We establish conditions for root-n consistency and asymptotic linearity by deriving second-order remainder bounds. We also develop flexible tests for assessing identification assumptions, including a doubly robust testing procedure, within a semiparametric extension of the front-door model that encodes generalized (Verma) independence constraints. We further show how these constraints can be leveraged to improve the efficiency of causal effect estimators. Simulation studies confirm favorable finite-sample performance, and real-data applications in education and emergency medicine illustrate the practical utility of our methods.
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