发表机构
Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出目标试验框架，将APIM关联问题转化为因果二元分析，揭示倾向得分分解偏差，并通过模拟和HRS数据验证其诊断价值。
AI 中文摘要
行动者-伴侣相互依赖模型（APIMs）描述了一个成员的暴露如何与该成员自身及其伴侣的结果相关联，但行动者和伴侣系数仅在额外的设计和识别假设下才具有因果性。我们在二元内干扰下将APIM式问题表述为二元目标试验，并明确建立了关联性表述与因果性表述之间的三个联系。第一，在联合可交换性、积极性以及同质且正确设定的治疗相关条件均值下，APIM的行动者、伴侣、交互和联合系数等于相应的因果对比。第二，对于二元二元治疗，将真实的成员特定倾向得分相乘会诱导出一个精确的单参数差异delta(H) = Cov(A_1, A_2 | H)：四个联合单元误差为(+delta, -delta, -delta, +delta)，总变异为2|delta|，且IPW对比偏差具有闭式表示。第三，相同的差异通过其与结果回归误差的乘积影响AIPW。一项36场景模拟和真实边际分解诊断表明，在残差依赖下，大的分离倾向得分偏差主要是分解偏差，而灵活的AIPW减少了非线性模型偏差，但未能消除弱支持不稳定性。在最强的依赖条件下，即使分解真实边际也会反转溢出对比，并将交互效应膨胀至接近其真实值的五倍。我们将该框架应用于2,400对健康与退休研究（HRS）夫妇，研究退休转变及随后的抑郁症状。联合退休较为罕见，残差治疗依赖显著，且估计对支持范围和倾向得分下限敏感。本研究的贡献在于建立了一座从APIM问题到因果二元分析的设计与诊断桥梁，而非提出新的通用干扰或AIPW理论。
英文摘要
Actor-partner interdependence models (APIMs) describe how one member's exposure is associated with that member's and the partner's outcomes, but actor and partner coefficients are causal only under additional design and identification assumptions. We formulate APIM-style questions as dyadic target trials under within-dyad interference and make three links between the associational and causal formulations explicit. First, under joint exchangeability, positivity, and a homogeneous correctly specified treatment-related conditional mean, APIM actor, partner, interaction, and joint coefficients equal the corresponding causal contrasts. Second, for binary dyadic treatment, multiplying the true member-specific propensities induces an exact one-parameter discrepancy delta(H) = Cov(A_1, A_2 | H): the four joint-cell errors are (+delta, -delta, -delta, +delta), total variation is 2|delta|, and IPW contrast bias has a closed-form representation. Third, the same discrepancy affects AIPW through its product with outcome-regression error. A 36-scenario simulation and a true-marginal factorization diagnostic show that large separate-propensity bias under residual dependence is primarily factorization bias, while flexible AIPW reduces nonlinear-model bias but not weak-support instability. In the strongest dependence condition, factorizing even the true marginals reversed the spillover contrast and inflated the interaction to nearly five times its true value. We apply the framework to 2,400 Health and Retirement Study couples studying retirement transitions and later depressive symptoms. Joint retirement was rare, residual treatment dependence was substantial, and estimates were sensitive to support and propensity flooring. The contribution is a design-and-diagnostic bridge from APIM questions to causal dyadic analyses, not a new general interference or AIPW theory.
Comments49 pages, 5 figures
