A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

2026-08-13

AI Summary

Existing world models struggle to explicitly represent entity attributes, interaction relations, and causal structures in the environment, limiting the reliability of prediction and decision-making. This work proposes a Causal World Model (CWM) that integrates causal representation learning, object-centric modeling, structural causal models, and causal discovery into a unified theoretical framework endowed with both generative capacity and interpretable causal mechanisms. The framework bridges perception, conceptual representation, and dynamics modeling, clarifies the model’s role in decision tasks, and characterizes the identifiability conditions and equivalence classes recoverable from observational data, thereby establishing a theoretical foundation for robust causal reasoning and decision-making.

Abstract

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.

Problem

• Causal World Models
• causal representation learning
• structural causal models
• world modeling
• causal reasoning

Innovation

• Causal World Models
• Causal Representation Learning
• Structural Causal Models
• Identifiability
• Object-Centric Learning