Position: Not Every Action Is an Intervention – Toward Causally Grounded World Models

World models (WMs) have become increasingly capable of predicting and generating how environments evolve, yet predictive accuracy alone does not guarantee reliable reasoning about how the world responds when its variables or mechanisms are deliberately changed. We argue that addressing this gap requires treating causal interventions as a first-class consideration in world modeling. Importantly, actions and interventions should not be conflated: actions can have valid interventional semantics under appropriate assumptions, but the intervention space relevant to understanding a world may differ from and extend beyond the agent's action space. We propose viewing next-generation WMs as intervention engines: systems that represent where interventions enter a causal system, how their effects propagate through its mechanisms, and how informative interventions can reduce uncertainty about those mechanisms. This perspective shifts the emphasis from predicting trajectories under observed conditions toward reasoning about the consequences of deliberate changes. We develop a conceptual framework and identify open problems spanning causal representation learning, intervention-driven exploration, mechanism uncertainty, counterfactual reasoning, and interventional evaluation. Our goal is not to prescribe a particular causal architecture, but to argue for a broader formulation of world modeling centered on meaningful intervention spaces. Such a formulation provides a path toward WMs that combine predictive capability with more reliable reasoning under interventions, with implications for planning, embodied AI, and scientific discovery.