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

TL;DR

This paper introduces a robustness test for sibling fixed effects models to detect outcome-to-outcome interference, which can bias causal estimates, and demonstrates its effectiveness through simulations.

Contribution

It proposes a novel test to identify outcome-to-outcome spillover in linear sibling fixed effects models, addressing a key bias source.

Findings

The test signals spillover when the FE correlates with confounders or treatments.

It accurately detects interference in various simulated scenarios.

The test fails if outcomes influence treatments or effects differ between siblings.

Abstract

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.