Symmetries and Causality: Causal Effect Identification Beyond IID Data
Abstract
We develop theory for the identification of complex causal queries from non-IID data. We propose a formal description of stochastic systems based on symmetries in data leaving causal mechanisms invariant. These symmetries become the source of statistical repetition for identification and mandate knowledge-transfer from observations to queries. The result is an abstract, simple and very general language for causal reasoning. We give descriptions of models and queries, and the formal infrastructure for their mathematically rigorous identification. The proposed structure enables a number of simplifying principles: A shared description of mechanisms and interventions, a duality of observed and queried structure, and the sub-division of the identification task into three steps - extraction, computation and query-expression. Our approach reproduces and matches standard theoretical results on IID data and transport of experimental and non-experimental data. By design, it additionally applies beyond IID data and captures complex causal queries not expressible via do- or soft-interventions.
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BibTeX
@misc{rabel2026symmetries,
title = {{Symmetries and Causality: Causal Effect Identification Beyond IID Data}},
author = {Martin Rabel and Jakob Runge},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.03697},
eprint = {2609.03697},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03697},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Symmetries and Causality: Causal Effect Identification Beyond IID Data
AU - Martin Rabel
AU - Jakob Runge
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2609.03697
UR - https://arxiv.org/abs/2609.03697
AB - We develop theory for the identification of complex causal queries from non-IID data. We propose a formal description of stochastic systems based on symmetries in data leaving causal mechanisms invariant. These symmetries become the source of statistical repetition for identification and mandate knowledge-transfer from observations to queries. The result is an abstract, simple and very general language for causal reasoning. We give descriptions of models and queries, and the formal infrastructure for their mathematically rigorous identification. The proposed structure enables a number of simplifying principles: A shared description of mechanisms and interventions, a duality of observed and queried structure, and the sub-division of the identification task into three steps - extraction, computation and query-expression. Our approach reproduces and matches standard theoretical results on IID data and transport of experimental and non-experimental data. By design, it additionally applies beyond IID data and captures complex causal queries not expressible via do- or soft-interventions.
ER -