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}.

翻译：基于LLM的智能体模拟越来越多地用于研究社会涌现现象，但宏观结果背后的微观到宏观因果机制通常仍不清晰。这具有挑战性，因为涌现源于交织的智能体交互、中层反馈和非线性，使得生成机制难以解析。为此，我们提出\textbf{\textsc{CAMO}}，一个从LLM智能体模拟中\textbf{微}观行为到\textbf{宏}观涌现的自动化\textbf{因}果发现框架。\textsc{CAMO}将机制假设转化为基于模拟记录的可计算因子，并学习以涌现目标变量$Y$为中心的紧凑因果表示。\textsc{CAMO}输出可计算的马尔可夫边界和最小上游解释子图，提供可解释的因果链和可操作的干预杠杆。它利用模拟器内部的反事实探测来定向模糊边并通过证据反驳当前观点时修正假设。在四种涌现场景下的实验展示了\textsc{CAMO}的潜力。