The Promises of Parallel Outcomes
Ying Zhou, Dingke Tang, Dehan Kong, Linbo Wang

TL;DR
This paper introduces a novel causal inference method using multiple outcomes, assuming their conditional independence, to address unmeasured confounding in observational studies, with theoretical and empirical validation.
Contribution
It proposes a symmetric, parallel outcomes approach for causal inference, providing nonparametric identifiability and parametric estimation under linear models.
Findings
Nonparametric identifiability with three or more outcomes
Effective estimation demonstrated on synthetic data
Validated approach on real observational data
Abstract
A 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. The key assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in our key identification 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.
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Bayesian Modeling and Causal Inference
