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We extend the framework of causal Kripke models in [8] to a probabilistic setting, by allowing a quantitative representation of a causal agent’s uncertainty. This framework incorporates probabilities into the Halpern-Pearl model of causality, enabling the evaluation of how likely an event is to be the actual cause of another. It also provides a structured approach to reason about causality in scenarios involving multiple possibilities, uncertainty, and knowledge. Furthermore, we illustrate that this framework is suitable for causal analysis in different probabilistic scenarios by providing several examples.

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Probabilistic Causal Kripke Models

  • 会议论文

  • 发表日期：

    2026年

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  • Yiwen Ding,
  • Krishna Manoorkar,
  • Apostolos Tzimoulis,
  • Ruoding Wang

摘要

We extend the framework of causal Kripke models in [8] to a probabilistic setting, by allowing a quantitative representation of a causal agent’s uncertainty. This framework incorporates probabilities into the Halpern-Pearl model of causality, enabling the evaluation of how likely an event is to be the actual cause of another. It also provides a structured approach to reason about causality in scenarios involving multiple possibilities, uncertainty, and knowledge. Furthermore, we illustrate that this framework is suitable for causal analysis in different probabilistic scenarios by providing several examples.