CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
This provides a benchmark for researchers in causal inference and dynamical systems to develop robust algorithms, though it is incremental as it builds on existing causal discovery methods.
The authors tackled the challenge of causal discovery in dynamical systems by introducing CausalDynamics, a large-scale benchmark and data generation framework, which includes true causal graphs from thousands of equations and climate models, and they evaluated state-of-the-art algorithms on noisy, confounded, and lagged dynamics.
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.