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Sibling fixed effects (FE) models are useful for estimating causal treatmenteffects while offsetting unobserved sibling-invariant confounding. However,treatment estimates are biased if an individual's outcome affects theirsibling's outcome. We propose a robustness test for assessing the presence ofoutcome-to-outcome interference in linear two-sibling FE models. We regress again-score--the difference between siblings' continuous outcomes--on bothsiblings' treatments and on a pre-treatment observed FE. Under certainrestrictions, the observed FE's partial regression coefficient signals thepresence of outcome-to-outcome interference. Monte Carlo simulationsdemonstrated the robustness test under several models. We found that anobserved FE signaled outcome-to-outcome spillover if it was directly associatedwith an sibling-invariant confounder of treatments and outcomes, directlyassociated with a sibling's treatment, or directly and equally associated withboth siblings' outcomes. However, the robustness test collapsed if the observedFE was directly but differentially associated with siblings' outcomes or ifoutcomes affected siblings' treatments.

Assessing Outcome-to-Outcome Interference in Sibling Fixed Effects Models