2021/09/27 by David C. Mallinson, Mallinson, David C.
Economics, Econometrics and Finance · Mathematics · Medicine · Psychology · #Advanced Causal Inference Techniques #Cognitive Abilities and Testing #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Global Maternal and Child Health #Methodology (stat.ME) #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.2109.13399
Version 2 Updates: Fixed typo in abstract; fixed typo in Table 1
openalex publication_date 2021/09/27 · arxiv created 2021/11/07 · arxiv updated 2021/11/09 · openalex created_date 2023/02/16 · openalex updated_date 2026/07/28
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.