Deciphering interventional dynamical causality from non-intervention complex systems
从非干预复杂系统中解析干预型动力因果关系AI 解读 ↓
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从非干预复杂系统中解析干预型动力因果关系
- 研究问题
- 在没有真实干预或已知动力模型的复杂系统中，能否仅凭观测时间序列识别干预意义下的因果联系并度量其强度？
- 方法与思路
- 作者提出干预型动力因果框架，并在延迟嵌入空间定义干预嵌入熵，以观测序列计算因果信息流及构建因果网络。
- 材料陈述的贡献
- 摘要称该方法有理论与数值支持，能够识别因果边、减轻混杂影响并度量因果强度，还报告了真实任务中的应用。
- 阅读关注点
- 应核对观测信息对应干预效应的识别条件、延迟嵌入要求、混杂处理范围，以及因果强度定义和与传统指标的比较设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实；摘要未列出理论条件，不能据此认定任意观测系统均可消除混杂或识别干预效应。
原文摘要
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. Delay-embedding technique provides a promising approach. In this study, we propose a framework named Interventional Dynamical Causality (IntDC) in contrast to the traditional Constructive Dynamical Causality (ConDC). ConDC, including Granger causality, transfer entropy and convergence of cross-mapping, measures the causality by constructing a dynamical model without considering interventions. A computational criterion, Interventional Embedding Entropy (IEE), is proposed to measure causal strengths in an interventional manner. IEE is an intervened causal information flow but in the delay-embedding space. Further, the IEE theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data, without requiring any knowledge of dynamical models or real interventions in the considered system. In particular, IEE can be applied to rank causal effects according to their importance and construct causal networks from data. We conducted numerical experiments to demonstrate that IEE can find causal edges accurately, eliminate effects of confounding, and quantify causal strength robustly over traditional indices. We also applied IEE to real-world tasks. IEE performed as an accurate and robust tool for causal analyses solely from the observational data. The IntDC framework and IEE algorithm provide an efficient approach to the study of causality from time series in diverse non-intervention complex systems.