因果世界模型的统一视角：从观测到表征再到结构
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文从因果视角研究不同抽象层级的世界模型，提出因果世界模型的形式定义，关联相关领域工作并阐明其组件可从数据恢复的条件，为世界模型奠定支持因果推理与决策的基础。
AI中文摘要：
世界模型（World Models, WM）正日益被视为智能体的基础，可使其在训练分布之外进行预测、规划与行动。本文从因果视角研究不同抽象层级的世界模型，涵盖从感知观测到构建环境动态规律的概念表征。本文提出，实用的世界模型不应仅具备生成能力，还需捕获决定并解释系统动态的实体属性、实体间交互及实体与环境间交互。本文基于世界模型拟支持的任务，给出因果世界模型（Causal World Models, CWM）的形式定义，将世界建模与因果表征学习、以对象为中心的学习、因果发现、结构因果模型及基于模型的决策等现有工作相连接。最后，本文将因果世界模型与可识别性文献关联，阐明何时可从数据中恢复世界模型的各组件，以及恢复至何种等价程度。由此，本文将世界模型建立在支持因果推理与明智决策的表征和结构之上。
英文摘要：
World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics. We argue that useful WMs must go beyond generative capabilities alone: they should also capture entity properties, entity-to-entity interactions, and entity-to-environment interactions that determine and explain the dynamics of a system. We provide a formal definition of Causal WMs (CWMs) grounded in the tasks they are intended to support, connecting world modelling with existing work in causal representation learning, object-centric learning, causal discovery, structural causal models, and model-based decision-making. Finally, we relate CWMs to the literature on identifiability, clarifying when the components of a WM can be recovered from data and up to which equivalence. With this, we ground WMs in representations and structures that support causal reasoning and informed decision-making.
