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Prospective and retrospective causal inferences based on the potential outcome framework

Author

Listed:
  • 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)

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.

Suggested Citation

  • Geng Zhi & Zhang Chao & Wang Xueli & Liu Chunchen & Wei Shaojie, 2024. "Prospective and retrospective causal inferences based on the potential outcome framework," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-15.
  • Handle: RePEc:bpj:causin:v:12:y:2024:i:1:p:15:n:1001
    DOI: 10.1515/jci-2023-0063
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