Promises of parallel outcomes

Ying Zhou, Dingke Tang, Dehan Kong and Linbo Wang

Biometrika, 2024, vol. 111, issue 2, 537-550

Abstract: SummaryA key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric; hence, the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.

Keywords: Some key words: Causal inference; Latent confounding; Multivariate outcome; Nonparametric identification (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:111:y:2024:i:2:p:537-550.

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