Sibling fixed effects (FE) models are useful for estimating causal treatment effects while offsetting unobserved sibling-invariant confounding. However, treatment estimates are biased if an individual's outcome affects their sibling's outcome. We propose a robustness test for assessing the presence of outcome-to-outcome interference in linear two-sibling FE models. We regress a gain-score--the difference between siblings' continuous outcomes--on both siblings' treatments and on a pre-treatment observed FE. Under certain restrictions, the observed FE's partial regression coefficient signals the presence of outcome-to-outcome interference. Monte Carlo simulations demonstrated the robustness test under several models. We found that an observed FE signaled outcome-to-outcome spillover if it was directly associated with an sibling-invariant confounder of treatments and outcomes, directly associated with a sibling's treatment, or directly and equally associated with both siblings' outcomes. However, the robustness test collapsed if the observed FE was directly but differentially associated with siblings' outcomes or if outcomes affected siblings' treatments.

翻译：固定效果(FE)模型有助于估计因果关系,同时抵消未观察到的兄弟姐妹间差别和观察到的FE。但是,如果一个人的结果影响其兄弟姐妹的结果,那么治疗估计就带有偏颇性。我们提议对线性双胞胎FE模型中结果对结果的干扰进行评估的稳健性测试。我们减少兄弟姐妹间持续结果对兄弟姐妹间待遇和所观察到的FE的预处理结果之间的差异。但是,在某些限制下,观察到的FE部分回归系数表示结果对结果的干扰。Monte Carlo模拟显示了几个模型下的稳健性测试。我们发现,观察到的FE表示结果对结果的外溢效应,如果它直接与兄弟姐妹间结果或影响兄弟姐妹间结果的治疗直接相关联,或者如果观察到的FE与兄弟姐妹间结果有直接但有差别的联系,那么稳健性测试就会崩溃。