Learning Implicit Causal World Models from Multi-Agent Demonstrations

2026-07-28

AI Summary

This work addresses the challenge that world models in multi-agent systems often conflate statistical correlations with causal mechanisms, leading to performance degradation under distributional shifts. The authors propose an implicit causal world model that does not require a predefined causal graph and instead leverages only offline multi-agent demonstrations. By introducing policy variance in partially observable environments, the approach ensures that environmental dynamics satisfy the sequential backdoor criterion, thereby enabling identifiability of the underlying causal structure. Integrating implicit causal modeling, the sequential backdoor criterion, and offline reinforcement learning, the method learns interpretable causal representations across tasks such as Two-Door, Navigation, and Giveaway. Notably, model accuracy improves significantly with increasing intervention strength, demonstrating the efficacy of the proposed framework in capturing true causal relationships.

Abstract

In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem is exacerbated in multi-agent systems where physical transitions are intertwined with strategic agent intents, causing world models to fail under distribution shift. We introduce Implicit Causal World Models to recover environmental dynamics from offline demonstrations without requiring pre-defined causal graphs. By incorporating policy variance, we render world models discoverable via the sequential backdoor condition. Evaluations across coordination tasks (Two-Door, Navigation, and Giveway) demonstrate that these models provide interpretable causal representations under both full and partial observability, with model accuracy scaling directly with interventional strength.

Problem

• causal world models
• multi-agent systems
• distribution shift
• statistical correlations
• environmental dynamics

Innovation

• Implicit Causal World Models
• multi-agent demonstrations
• sequential backdoor condition
• policy variance
• causal representation