CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations

2026-04-16

AI Summary

This work addresses the challenge of uncovering causal mechanisms underlying macroscopic emergent phenomena in large language model (LLM) agent systems, which arise from complex microscopic interactions and nonlinear dynamics and are thus difficult to interpret. To this end, the paper introduces CAMO, a novel framework that, for the first time, automatically discovers interpretable causal chains linking micro-level agent behaviors to macro-level emergence. CAMO formalizes mechanistic hypotheses as computable factors, learns the Markov boundary of a target variable Y along with its minimal upstream explanatory subgraph, and employs in-simulator counterfactual probes to directionally disambiguate causal edges and refine hypotheses. Experiments across four canonical emergence scenarios demonstrate that CAMO efficiently identifies concise, interpretable, and intervention-supportive causal mechanisms, offering a new paradigm for causal understanding and control of complex agent systems.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often remain unclear. This is challenging because emergence arises from intertwined agent interactions and meso-level feedback and nonlinearity, making generative mechanisms hard to disentangle. To this end, we introduce \textbf{\textsc{CAMO}}, an automated \textbf{Ca}usal discovery framework from \textbf{M}icr\textbf{o} behaviors to \textbf{M}acr\textbf{o} Emergence in LLM agent simulations. \textsc{CAMO} converts mechanistic hypotheses into computable factors grounded in simulation records and learns a compact causal representation centered on an emergent target $Y$. \textsc{CAMO} outputs a computable Markov boundary and a minimal upstream explanatory subgraph, yielding interpretable causal chains and actionable intervention levers. It also uses simulator-internal counterfactual probing to orient ambiguous edges and revise hypotheses when evidence contradicts the current view. Experiments across four emergent settings demonstrate the promise of \textsc{CAMO}.

Problem

• causal discovery
• emergence
• LLM agent simulations
• micro-to-macro mechanisms
• social emergence

Innovation

• Causal Discovery
• LLM Agent Simulation
• Macro Emergence
• Markov Boundary
• Counterfactual Probing