Counterfactual Prediction Under Selective Confounding
Sohaib Kiani,
Jared Barton,
Jon Sushinsky
et al.
Abstract:This research addresses the challenge of conducting interpretable causal inference between a binary treatment and its resulting outcome when not all confounders are known. Confounders are factors that have an influence on both the treatment and the outcome. We relax the requirement of knowing all confounders under desired treatment, which we refer to as Selective Confounding, to enable causal inference in diverse real-world scenarios. Our proposed scheme is designed to work in situations where multiple decisio… Show more
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