Geometric Causal Models
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric causal models (GCMs), a framework for causal inference from dependent data that exploits underlying symmetries of the data generating process. For example, in spatial data, we consider processes that are symmetric under translations, or in graph data, symmetric under permutations of the nodes. We show how symmetries, formalized with group theory, can enable causal identification and estimation. We deploy ergodic theory for amenable groups to establish identification, and combine geometric dee...
| Authors | Eli N. Weinstein, David M. Blei |
|---|---|
| Primary category | stat.ML |
| Published | 6 July 2026 |
| Last updated at source | 11 July 2026 |
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