Causal World Models via Disentangled Autonomous Dynamics and Agent Interventions

Predicting a transition does not uniquely determine which changes arise from autonomous environmental dynamics and which are associated with an agent's action. We introduce the Causal Disentanglement World Model (CDWM), a structured neural world model that assigns these contributions to an Environment Pathway and an Intervention Pathway. The recurrent environment branch excludes the current action, while the feed-forward intervention branch conditions on state and action. Their sum supports Monte Carlo tree search, and the magnitude of the intervention component defines an Agency Bonus for exploration. Under explicit assumptions including state sufficiency, a valid null-action anchor, action coverage, realizability, and global population risk minimization, the two conditional-mean components are identifiable at the population optimum. We evaluate CDWM on 26 Atari100k games using three seeded runs, together with branch-removal analyses, a Venture exploration ablation, representation diagnostics, and supplementary forced-action and continuous-control studies. The results support the practical value of a structured decomposition with task-dependent pathway reliance and improved exploration, while the encoder and both pathways remain learned black-box functions and the causal interpretation remains conditional on the stated assumptions.