Agent-Based Model (ABM) validation is crucial as it helps ensuring the reliability of simulations, and causal discovery has become a powerful tool in this context. However, current causal discovery methods often face accuracy and robustness challenges when applied to complex and noisy time series data, which is typical in ABM scenarios. This study addresses these issues by proposing a Robust Cross-Validation (RCV) approach to enhance causal structure learning for ABM validation. We develop RCV-VarLiNGAM and RCV-PCMCI, novel extensions of two prominent causal discovery algorithms. These aim to reduce the impact of noise better and give more reliable causal relation results, even with high-dimensional, time-dependent data. The proposed approach is then integrated into an enhanced ABM validation framework, which is designed to handle diverse data and model structures. The approach is evaluated using synthetic datasets and a complex simulated fMRI dataset. The results demonstrate greater reliability in causal structure identification. The study examines how various characteristics of datasets affect the performance of established causal discovery methods. These characteristics include linearity, noise distribution, stationarity, and causal structure density. This analysis is then extended to the RCV method to see how it compares in these different situations. This examination helps confirm whether the results are consistent with existing literature and also reveals the strengths and weaknesses of the novel approaches. By tackling key methodological challenges, the study aims to enhance ABM validation with a more resilient valuation framework presented. These improvements increase the reliability of model-driven decision making processes in complex systems analysis.

翻译：基于智能体模型（ABM）的验证至关重要，因为它有助于确保模拟的可靠性，而因果发现已成为该领域的有力工具。然而，当前的因果发现方法在处理复杂且含噪声的时间序列数据时，常常面临准确性和鲁棒性方面的挑战，而这正是ABM场景中的典型情况。本研究通过提出一种鲁棒交叉验证（RCV）方法来解决这些问题，以增强用于ABM验证的因果结构学习。我们开发了RCV-VarLiNGAM和RCV-PCMCI，这是两种著名因果发现算法的新颖扩展。这些方法旨在更好地减少噪声的影响，即使面对高维、时间相关的数据，也能提供更可靠的因果关系结果。随后，所提出的方法被集成到一个增强的ABM验证框架中，该框架旨在处理多样化的数据和模型结构。我们使用合成数据集和一个复杂的模拟fMRI数据集对该方法进行了评估。结果表明，该方法在因果结构识别方面具有更高的可靠性。本研究探讨了数据集的多种特性如何影响现有因果发现方法的性能，这些特性包括线性、噪声分布、平稳性以及因果结构密度。随后，我们将此分析扩展到RCV方法，以比较其在不同情境下的表现。这一检验有助于确认结果是否与现有文献一致，并揭示新方法的优势与不足。通过应对关键的方法学挑战，本研究旨在通过提出一个更具韧性的评估框架来增强ABM验证。这些改进提高了复杂系统分析中模型驱动决策过程的可靠性。