kausable/CausalDynamics

CausalDynamics是一个用于结构发现动态系统因果模型的大规模基准框架，旨在严格评估最新的因果发现算法在动态系统上的表现。该数据集包含了成千上万的耦合普通和随机微分方程以及两个理想化的气候模型的真实因果图，用于支持从简单到复杂的动态系统的因果发现挑战。

CausalDynamics is a large-scale benchmark framework for the structural discovery of dynamical causal models, designed to rigorously evaluate the performance of state-of-the-art causal discovery algorithms on dynamical systems. The dataset contains true causal graphs derived from thousands of coupled ordinary and stochastic differential equations as well as two idealized climate models, supporting causal discovery challenges ranging from simple to complex dynamical systems.