如果你在搞世界模型或因果推理，这篇把概念理清了，还连上了因果发现的现有工作，值得一看。
这篇论文从因果视角研究世界模型，提出超越生成能力、捕捉实体属性及其交互的因果世界模型（CWM）正式定义。作者将世界建模与因果表示学习、目标中心学习、因果发现、结构因果模型及基于模型的决策制定等工作联系起来。论文还探讨了可识别性文献，澄清世界模型组件何时能从数据中恢复以及等价性边界。
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
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.