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 CAMO, an automated Causal discovery framework from Micro behaviors to Macro Emergence in LLM agent simulations. CAMO converts mechanistic hypotheses into computable factors grounded in simulation records and learns a compact causal representation centered on an emergent target . 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 CAMO.[The code is available at an anonymous link: <https://anonymous.4open.science/r/CAMO-0E6C/>.]

Multi-agent simulation based on LLMs has increasingly emerged as a new paradigm for exploring complex social phenomena and validating theoretical hypotheses. However, traditional experimental design in the social sciences relies heavily on interdisciplinary expert knowledge, involving cumbersome procedures and high technical barriers. While LLM-driven agents demonstrate broad prospects for designing experiments, their limitations regarding reliability and scientific rigor continue to significantly hinder their in-depth application in social science research. To address these challenges, this paper proposes FSTS, an automated framework for multi-agent experiment design based on script generation. Drawing on the concept of the "Decision Theater," the framework deconstructs experimental design into three core phases: Script Composition, Script Finalization, and Actor Generation. Tests across multiple scenarios indicate that the agents generated by this framework can enact the script within the "experimental theater," reproducing results consistent with real-world situations. The proposal of FSTS not only effectively lowers the barrier for social science experimental design but also provides scientifically grounded decision support for policy-making.