Categorical Probability and Applied Topology in Causal Inference

In this project, we study category theoretic and topological approaches to developing more general models in causal inference.

We aim to extend the causal inference framework within categorical probability theory to time-varying data, and investigate how tools from applied topology such as sheaves on graphs or quiver representations can be used to model causal phenomena.