Prospective and retrospective causal inferences based on the potential outcome framework

Geng Zhi (), Zhang Chao (), Wang Xueli (), Liu Chunchen () and Wei Shaojie ()
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Geng Zhi: School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, P. R. China
Zhang Chao: School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, P. R. China
Wang Xueli: School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, P. R. China
Liu Chunchen: LingYang, Alibaba Group, Hangzhou, P. R. China
Wei Shaojie: School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, P. R. China

Journal of Causal Inference, 2024, vol. 12, issue 1, 15

Abstract: In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and the surrogate paradox, respectively. For retrospective causal inference, we introduce the concepts of posterior causal effects given observed evidence to quantify the causes of effects. The posterior causal effects provide a unified framework for deducing both effects of causes in prospective causal inference and causes of effects in retrospective causal inference. We compare the medical diagnostic approaches based on Bayesian posterior probabilities and posterior causal effects for classification and attribution.

Keywords: causal inference; cause of effect; effect of cause; potential outcome; surrogate paradox; Yule–Simpson paradox (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:15:n:1001

DOI: 10.1515/jci-2023-0063

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