从因果视角统一构建可解释的世界模型，支持智能体决策。
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
- 提出因果世界模型框架，融合实体属性与交互关系
- 明确模型组件可识别性边界，确保结构可恢复
- 适合研究因果推理与具身智能的学者参考
世界模型（WM）正成为智能体超越训练分布进行预测、规划和行动的基础。本文从因果视角出发，系统考察了从感知观测到概念化环境动态结构的多层抽象建模。我们主张，有效的世界模型不应仅具备生成能力，还需捕捉实体属性、实体间及实体与环境间的交互，以解释系统动态。为此，我们提出了基于任务目标的因果世界模型（CWM）形式化定义，将其与因果表示学习、以对象为中心的学习、因果发现、结构性因果模型及基于模型的决策等领域的已有工作相连接。最后，我们关联了可识别性理论，阐明了在何种条件下模型组件可以从数据中恢复及其等价程度。由此，我们将世界模型建立在支持因果推理与明智决策的表征与结构基础之上。
原文摘要 · Abstract (English)
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.
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