论文标题
复杂系统中因果关系的简单测试
A simple test for causality in complex systems
论文作者
论文摘要
我们为从观察结果推断因果关系的长期问题提供了新的解决方案，而无需对未知机制进行建模。我们表明，任何动力学系统的演变都与量化有限观察结果的因果关系的预测不对称性有关。内置的显着性标准可以避免替代测试并大大提高计算效率。我们验证对众多在自然界中通常发生行为的综合系统的测试，从线性和非线性随机过程到表现出非线性确定性混乱的系统以及具有已知地面真相的现实世界数据。应用于有争议的冰川间 - 冰川海平面和CO $ _ {2} $锁定状态的问题，我们的测试在过去的80万年中发现了Co $ _ {2} $的经验证据。我们的发现与使用时间序列研究自然系统的任何学科有关。
We provide a new solution to the long-standing problem of inferring causality from observations without modeling the unknown mechanisms. We show that the evolution of any dynamical system is related to a predictive asymmetry that quantifies causal connections from limited observations. A built-in significance criterion obviates surrogate testing and drastically improves computational efficiency. We validate our test on numerous synthetic systems exhibiting behavior commonly occurring in nature, from linear and nonlinear stochastic processes to systems exhibiting nonlinear deterministic chaos, and on real-world data with known ground truths. Applied to the controversial problem of glacial-interglacial sea level and CO$_{2}$ evolving in lock-step, our test uncovers empirical evidence for CO$_{2}$ as a driver of sea level over the last 800 thousand years. Our findings are relevant to any discipline where time series are used to study natural systems.
