arXiv cs.AI· Martin Rabel, Jakob Runge·· 12 小时前AI 评分31
Symmetries and Causality：超越 IID 数据的因果效应识别理论
Symmetries and Causality: Causal Effect Identification Beyond IID Data
AI 导读
一篇 arXiv 论文提出基于对称性的因果效应识别理论，用使因果机制保持不变的数据对称性作为统计重复来源，从而从非 IID 数据中识别复杂因果查询。该框架将识别任务拆分为提取、计算和查询表达三步，可复现 IID 数据及实验与非实验数据迁移的标准理论结果，并能表达 do- 或软干预无法描述的复杂因果查询。
正文 · 原文
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.
| Subjects: | Statistics Theory (math.ST); Artificial Intelligence (cs.AI) |
| MSC classes: | 62A09, 68T37 |
| Cite as: | arXiv:2609.03697 [math.ST] |
| (or arXiv:2609.03697v2 [math.ST] for this version) | |
| https://doi.org/10.48550/arXiv.2609.03697 arXiv-issued DOI via DataCite |
Submission history
From: Martin Rabel [view email]
[v1]
Thu, 3 Sep 2026 11:34:44 UTC (153 KB)
[v2]
Tue, 20 Oct 2026 13:30:30 UTC (169 KB)
来源：arXiv cs.AI · arxiv.org