Beyond Actor-Partner Associations: A Target-Trial Framework for Causal Spillover Effects in Longitudinal Dyadic Data
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超越行动者—伴侣关联：纵向双人数据因果溢出效应的目标试验框架
- 研究问题
- 在双人组内存在干扰且双方处理相关时，如何把行动者—伴侣关联问题转为因果目标试验，并诊断倾向评分分解偏误？
- 方法与思路
- 作者明确关联系数等于因果对比的条件，分析联合处理概率被边际概率乘积替代时的IPW与AIPW偏误，并开展模拟和夫妻应用。
- 材料陈述的贡献
- 摘要称建立从APIM问题到因果双人分析的设计与诊断桥梁，指出联合概率分解偏误和弱支持问题，未声称提出通用干扰理论。
- 阅读关注点
- 应核对联合可交换性、正值性及均值模型条件，联合倾向评分的估计与诊断，并检查稀少联合退休和概率截底的敏感性。
- 阅读边界
- 仅据arXiv v1预印本摘要，不能确认同行评审或接收。结果依赖所述识别条件；灵活AIPW不能据此视为解决弱支持或所有处理依赖问题。
原文摘要
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.