Categoroids: Universal Conditional Independence
This work provides a foundational framework for AI, causal inference, and statistics, but it is incremental as it builds on existing algebraic concepts to unify conditional independence representations.
The paper tackles the problem of characterizing universal properties of conditional independence by introducing categoroids, an algebraic structure that combines binary and ternary structures to abstractly represent conditional independence across different axiom sets like graphoids and separoids.
Conditional independence has been widely used in AI, causal inference, machine learning, and statistics. We introduce categoroids, an algebraic structure for characterizing universal properties of conditional independence. Categoroids are defined as a hybrid of two categories: one encoding a preordered lattice structure defined by objects and arrows between them; the second dual parameterization involves trigonoidal objects and morphisms defining a conditional independence structure, with bridge morphisms providing the interface between the binary and ternary structures. We illustrate categoroids using three well-known examples of axiom sets: graphoids, integer-valued multisets, and separoids. Functoroids map one categoroid to another, preserving the relationships defined by all three types of arrows in the co-domain categoroid. We describe a natural transformation across functoroids, which is natural across regular objects and trigonoidal objects, to construct universal representations of conditional independence.. We use adjunctions and monads between categoroids to abstractly characterize faithfulness of graphical and non-graphical representations of conditional independence.