Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2024 · Volume 86 · Issue 2

Abstract

Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about ψ -network dependence. Finally, we provide a consistent variance estimator.

Keywords

causal inference, causal inference with networks, interference, negative control, peer effect, proximal causal learning

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