World models need to learn causal structure and entity interactions, not just predict observations. This enables agents to reason about why things happen and make better decisions outside their training distribution.
This paper proposes a framework for understanding world models through a causal lens, arguing that effective world models should capture not just how to generate future observations, but also the underlying causal structure—entity properties, interactions, and dynamics—that explains environmental behavior.