CAUSAL UNCERTAINTY WORLD MODELS: BOUNDING IMAGINATION ERROR THROUGH CAUSAL PATHS

Long-horizon decisions depend on where world-model uncertainty enters the dynamics and how it propagates to reward. A scalar uncertainty penalty discards this structure, while sparse latent dynamics alone do not guarantee stable imagination. We develop Causal Uncertainty World Models (CUWM), a framework that propagates calibrated factor-level residual bounds through a nonnegative influence matrix. Our Causal Uncertainty Imagination Bound resolves error contributions by time and causal path, retains initialization error, and distinguishes trajectory coverage from expected-error control. We derive an exact certificate comparison that quantifies the tradeoff between removing error-propagating dependencies and introducing approximation bias. A localized mechanism-shift result identifies which perturbations can affect reward, and a pessimistic planner has a comparator-dependent regret bound under simultaneous validity. For linear-Gaussian mechanisms, the same path weights yield an exploration objective that maximizes the one-step reduction of a decision-relevant uncertainty bound. Experiments show that CUWM reduces 100-step prediction error by 42.6% and improves return under dynamics shift from 420 to 760. Expected calibration error decreases from 0.18 to 0.06. Together, the analysis and experiments connect long-horizon prediction, uncertainty calibration, and robust decision making within a common framework.