Robust Time Series Causal Discovery for Agent-Based Model Validation
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用于主体模型验证的稳健时间序列因果发现
- 研究问题
- 在主体模型产生的复杂、含噪时间序列中，如何更可靠地识别因果结构，并判断线性、平稳性及网络稠密程度如何影响验证表现？
- 方法与思路
- 作者提出稳健交叉验证方法，将其嵌入 VarLiNGAM 与 PCMCI 算法，并使用合成数据及复杂的模拟 fMRI 数据评估结构识别表现。
- 材料陈述的贡献
- 摘要称，两种扩展算法提高了因果结构识别的可靠性，并系统考察噪声分布、线性、平稳性与结构稠密程度对既有及新方法的影响。
- 阅读关注点
- 应核对交叉验证如何保留时间依赖、因果图的识别假设、结构准确性的评价指标，以及模拟数据上的表现能否支持实际主体模型验证。
- 阅读边界
- 所给来源为 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明具体识别条件、改进幅度或真实数据验证，可靠性结论有待核对。
原文摘要
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.