The d-separation criterion in Categorical Probability

Tobias Fritz; Andreas Klingler

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arXiv · 2207.05740

The d-separation criterion in Categorical Probability

Tobias Fritz·Andreas Klingler

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Abstract

The d-separation criterion detects the compatibility of a joint probability distribution with a directed acyclic graph through certain conditional independences. In this work, we study this problem in the context of categorical probability theory by introducing a categorical definition of causal models, a categorical notion of d-separation, and proving an abstract version of the d-separation criterion. This approach has two main benefits. First, categorical d-separation is a very intuitive criterion based on topological connectedness. Second, our results apply both to measure-theoretic probability (with standard Borel spaces) and beyond probability theory, including to deterministic and possibilistic networks. It therefore provides a clean proof of the equivalence of local and global Markov properties with causal compatibility for continuous and mixed random variables as well as deterministic and possibilistic variables.

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Tobias Fritz, Andreas Klingler. 2022-07-12. The d-separation criterion in Categorical Probability. https://arxiv.org/abs/2207.05740

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