Problem
Research questions and friction points this paper is trying to address.
Symmetries
Causality
IID Data
Causal Reasoning
Symmetries and Causality: Causal Effect Identification Beyond IID Data
🤖 AI Summary
本文提出基于数据对称性保持因果机制不变的形式化描述方法，以解决复杂机器学习任务中的因果推理问题，超越了IID数据的限制。
📝 Abstract
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 on IID data and transport of experimental and non-experimental data. But its main purpose is to unify and substantially extend the scope of causal reasoning, in going beyond IID data and in approaching complex causal queries not captured by do- or soft-interventions. This new perspective on causally relevant aspects of data-modeling additionally sheds new light on well-known structures like c-components or hedges but also includes aspects of missing data and is inherently well-suited for the description of transfer and robustness properties.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Symmetries
Causal Reasoning
Beyond IID Data
Complex Causal Queries
Data Modeling
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