AI RESEARCH

Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability

arXiv CS.AI

ArXi:2603.14169v1 Announce Type: cross Average treatment effects (ATE) and conditional average treatment effects (CATE) are foundational causal estimands, but they target changes in expected outcomes and can miss treatment-induced changes in the shape of outcome distributions. A canonical failure mode occurs when control outcomes are unimodal, treated outcomes become bimodal, and both distributions have the same mean. In such cases mean-based causal estimands are zero even though the geometry and topology of the outcome law change substantially.