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Markov categories and the inflation technique for latent-variable causal inference

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CIFW01 - Foundations of causal inference

Markov categories have recently gained prominence as a higher-level language for probability and statistics, and for causal inference in particular. This language facilitates a treatment of continuous variables and general measure-theoretic probability with the same ease as for discrete variables and facilitates clear and general proofs of many qualitative results (such as the d-separation criterion or certain 0/1-laws). In this talk, I will give a gentle introduction to Markov categories, and subsequently illustrate the formalism by presenting the inflation technique in terms of Markov categories. The inflation technique is a hierarchy of tests which are necessary and (in the limit) sufficient conditions to decide the compatibility of a given probability distribution with a given causal structure with latent variables.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Cambridge University Engineering Department, Lecture Room 12 Average on weekdays: 8928.53295669 kWh Webinar & LT2, Computer Laboratory, William Gates Building.

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