CAMO：一种用于从微观行为到宏观涌现的LLM代理模拟自动因果发现的框架
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
AI总结：
CAMO通过分析LLM代理模拟中的微观行为，自动发现导致宏观结果的因果机制，提供可解释的因果链和干预手段，提升对社会涌现的理解。
摘要：
LLM-赋能的代理模拟越来越多地用于研究社会涌现，但宏观结果背后的微观到宏观因果机制往往不清楚。这是因为涌现来自交织的代理互动和中层反馈和非线性，使生成机制难以分离。为此，我们引入CAMO，一种从微观行为到宏观涌现的自动因果发现框架。CAMO将机制假设转化为基于模拟记录的可计算因素，并学习以涌现目标Y为中心的紧凑因果表示。CAMO输出可计算的马尔可夫边界和最小上游解释子图，产生可解释的因果链和可操作的干预杠杆。它还利用模拟器内部的反事实探测来定向模糊边并修订假设，当证据与当前观点矛盾时。在四个涌现设置上的实验展示了CAMO的潜力。
英文摘要：
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}.
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