Symmetries and Causality: Causal Effect Identification Beyond IID Data
In the natural sciences, symmetries and cause-effect relationships are ubiquitous. Yet for complex machine-learning tasks, like world-modeling in reinforcement learning, they appear difficult to harness. We propose a formal description of statistical systems based on symmetries in data leaving causal mechanisms invariant. The result is an abstract, simple and general mathematical language for causal reasoning. This paper provides formal descriptions of models and queries, setting up this language, and the formal infrastructure and strategies for their mathematically rigorous identification from data within this formalism. This approach reproduces and matches standard theoretical results o...
| Authors | Martin Rabel, Jakob Runge |
|---|---|
| Primary category | math.ST |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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