Causal Homotopy

TL;DR

This paper introduces a topological framework using Alexandroff spaces to characterize homotopical equivalences in causal DAG models, unifying various algorithms and significantly reducing the search space for causal discovery.

Contribution

It develops a novel topological approach to represent posets and DAGs, enabling more efficient causal structure learning and unification of existing methods.

Findings

Reduces the search space for DAG structures by orders of magnitude.

Unifies disparate causal discovery algorithms through topological representation.

Demonstrates the effectiveness of topology-based constraints in causal inference.

Abstract

We characterize homotopical equivalences between causal DAG models, exploiting the close connections between partially ordered set representations of DAGs (posets) and finite Alexandroff topologies. Alexandroff spaces yield a directional topological space: the topology is defined by a unique minimal basis defined by an open set for each variable x, specified as the intersection of all open sets containing x. Alexandroff spaces induce a (reflexive, transitive) preorder. Alexandroff spaces satisfying the Kolmogorov T0 separation criterion, where open sets distinguish variables, converts the preordering into a partial ordering. Our approach broadly is to construct a topological representation of posets from data, and then use the poset representation to build a conventional DAG causal model. We illustrate our framework by showing how it unifies disparate algorithms and case studies proposed previously. Topology plays two key roles in causal discovery. First, topological separability constraints on datasets have been used in several previous approaches to infer causal structure from observations and interventions. Second, a diverse range ofgraphical models used to represent causal structures can be represented in a unified way in terms of a topological representation of the induced poset structure. We show that the homotopy theory of Alexandroff spaces can be exploited to significantly efficiently reduce the number of possible DAG structures, reducing the search space by several orders of magnitude.