Probabilistic Causal Kripke Models

Y. J. Ding

doi:10.1007/978-981-95-2481-5_4

Probabilistic Causal Kripke Models

2026-01-01

0

PRE

AI

Apostolos Tzimoulis *

王

王若丁 (Ruoding Wang) *

DOI：10.1007/978-981-95-2481-5_4

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Abstract

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We extend the framework of causal Kripke models in [8] toa 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.

Keywords:

Causal model

Kripke models

Probabilistic causality

Counterfactual reasoning

Probabilistic Causal Kripke Models

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