[2507.13920] Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem

Computer Science > Machine Learning arXiv:2507.13920 (cs) [Submitted on 18 Jul 2025 (v1), last revised 5 Apr 2026 (this version, v2)] Title:Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem Authors:Turan Orujlu, Christian Gumbsch, Martin V. Butz, Charley M Wu View a PDF of the paper titled Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem, by Turan Orujlu and 3 other authors View PDF HTML (experimental) Abstract:Most neural models of causality assume static causal graphs, failing to capture the dynamic and sparse nature of physical interactions where causal relationships emerge and dissolve over time. We introduce the Causal Process Framework and its neural implementation, Causal Process Models (CPMs), for learning sparse, time-varying causal graphs from visual observations. Unlike traditional approaches that maintain dense connectivity, our model explicitly constructs causal edges only when objects actively interact, dramatically improving both interpretability and computational efficiency. We achieve this by casting dynamic interaction-graph construction for world modeling as a multi-agent reinforcement learning problem, where specialized agents sequentially decide which objects are causally connected at each timestep. Our key innovation is a structured representation that factorizes object and force vectors along three learned dimensions (mutability, causal relevance, and...