The presence of intermediate confounders, also called recanting witnesses, is a fundamental challenge to the investigation of causal mechanisms in mediation analysis, preventing the identification of natural path-specific effects. Proposed alternative parameters (such as randomizational interventional effects) are problematic because they can be non-null even when there is no mediation for any individual in the population; i.e., they are not an average of underlying individual-level mechanisms. In this paper we develop a novel method for mediation analysis in settings with intermediate confounding, with guarantees that the causal parameters are summaries of the individual-level mechanisms of interest. The method is based on recently proposed ideas that view causality as the transfer of information, and thus replace recanting witnesses by draws from their conditional distribution, what we call "recanting twins". We show that, in the absence of intermediate confounding, recanting twin effects recover natural path-specific effects. We present the assumptions required for identification of recanting twins effects under a standard structural causal model, as well as the assumptions under which the recanting twin identification formulas can be interpreted in the context of the recently proposed separable effects models. To estimate recanting-twin effects, we develop efficient semi-parametric estimators that allow the use of data driven methods in the estimation of the nuisance parameters. We present numerical studies of the methods using synthetic data, as well as an application to evaluate the role of new-onset anxiety and depressive disorder in explaining the relationship between gabapentin/pregabalin prescription and incident opioid use disorder among Medicaid beneficiaries with chronic pain.

翻译：中间混杂（也称为“重述证人”）的存在是中介分析中研究因果机制的根本挑战，它阻碍了自然路径特定效应的识别。所提出的替代参数（如随机化干预效应）存在问题，因为即使群体中没有任何个体的中介效应存在，这些参数也可能非零，即它们并非潜在个体层面机制的平均值。本文开发了一种在存在中间混杂情况下进行中介分析的新方法，该方法保证了因果参数是所关注的个体层面机制的汇总。该方法基于近期提出的将因果关系视为信息传递的观点，从而用从条件分布中抽取的样本替代重述证人，我们称之为“重述双胞胎”。我们证明，在没有中间混杂的情况下，重述双胞胎效应能够恢复自然路径特定效应。我们展示了在标准结构因果模型下识别重述双胞胎效应所需的假设，以及在最近提出的可分离效应模型背景下重述双胞胎识别公式可被解释的假设。为了估计重述双胞胎效应，我们开发了高效半参数估计方法，允许在估计干扰参数时使用数据驱动方法。我们通过合成数据进行了数值研究，并应用于评估新发焦虑症和抑郁症在解释加巴喷丁/普瑞巴林处方与慢性疼痛医疗补助受益人群中阿片类药物使用障碍新发病例之间关系中的作用。