Markov categories, causal theories, and the do-calculus

TL;DR

This paper develops a category-theoretic framework for causal models that formalizes causal reasoning concepts and simplifies the do-calculus, making it more abstract, general, and conceptually clear.

Contribution

It introduces a canonical construction of Markov categories from DAGs, providing a purely causal and abstract foundation for causal reasoning and do-calculus.

Findings

Abstracts causal independence and separation in a category-theoretic setting.

Derives a simplified, syntactic version of Pearl's do-calculus.

Shows the simplified do-calculus is as powerful as the full version.

Abstract

We give a category-theoretic treatment of causal models that formalizes the syntax for causal reasoning over a directed acyclic graph (DAG) by associating a free Markov category with the DAG in a canonical way. This framework enables us to define and study important concepts in causal reasoning from an abstract and "purely causal" point of view, such as causal independence/separation, causal conditionals, and decomposition of intervention effects. Our results regarding these concepts abstract away from the details of the commonly adopted causal models such as (recursive) structural equation models or causal Bayesian networks. They are therefore more widely applicable and in a way conceptually clearer. Our results are also intimately related to Judea Pearl's celebrated do-calculus, and yield a syntactic version of a core part of the calculus that is inherited in all causal models. In particular, it induces a simpler and specialized version of Pearl's do-calculus in the context of causal Bayesian networks, which we show is as strong as the full version.