  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.
