构建大规模动态因果模型基准，助力复杂系统因果发现。
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
- 基于数千个微分方程生成可扩展的动态因果数据
- 涵盖噪声、混杂与滞后动态，评估主流算法性能
- 支持自定义耦合结构，适用于物理与气候等多领域
针对无法实施主动干预的动态系统，因果发现面临重大挑战。现有方法及基准多适用于确定性、低维和弱非线性时间序列。为此，我们提出CausalDynamics——一个大规模基准与可扩展的数据生成框架，用于推进动态因果模型的结构发现。该基准包含数千个由线性与非线性耦合的常微分方程及随机微分方程，以及两个理想化气候模型生成的真实因果图。我们在具有噪声、混杂和时滞动态的系统上，对当前最先进的因果发现算法进行综合评估。CausalDynamics提供即插即用、可自定义耦合的流程，支持构建多层次物理系统。我们预计该框架将促进跨领域鲁棒因果发现算法的发展。代码与文档已公开于https://kausable.github.io/CausalDynamics。
原文摘要 · Abstract (English)
Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associated benchmarks are tailored to deterministic, low-dimensional and weakly nonlinear time-series data. To address these limitations, we present CausalDynamics, a large-scale benchmark and extensible data generation framework to advance the structural discovery of dynamical causal models. Our benchmark consists of true causal graphs derived from thousands of both linearly and nonlinearly coupled ordinary and stochastic differential equations as well as two idealized climate models. We perform a comprehensive evaluation of state-of-the-art causal discovery algorithms for graph reconstruction on systems with noisy, confounded, and lagged dynamics. CausalDynamics consists of a plug-and-play, build-your-own coupling workflow that enables the construction of a hierarchy of physical systems. We anticipate that our framework will facilitate the development of robust causal discovery algorithms that are broadly applicable across domains while addressing their unique challenges. We provide a user-friendly implementation and documentation on https://kausable.github.io/CausalDynamics.
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