---
license: mit
---
# CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
NeurIPS 2025
A comprehensive benchmark framework designed to rigorously evaluate state-of-the-art causal discovery algorithms for dynamical systems.
## Key Features
1️⃣ **Large-Scale Benchmark**. Systematically evaluate state-of-the-art causal discovery algorithms on thousands of graph challenges with increasing difficulty.
2️⃣ **Customizable Data Generation**. Scalable, user-friendly generation of increasingly complex coupled ordinary and stochastic systems of differential equations
3️⃣ **Diverse Challenges**. From simple chaotic systems to modular causal coupling of dynamical systems, including optional noise, confounding, time lags, and even climate model dynamics.
**Abstract**: 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 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.
## Datasets
You can generate your own dataset (see [getting started](#getting-started)), but you can also download our preprocessed ones directly from HuggingFace:
```bash
wget https://huggingface.co/datasets/kausable/CausalDynamics/resolve/main/process_causaldynamics.py
python process_causaldynamics.py
```
## Installation
### Using pip
*CausalDynamics* is available on [PyPi](https://pypi.org/project/causaldynamics/), so you can use pip to install `causaldynamics`, which currently requires Python version `3.10`.
```bash
pip install causaldynamics
```
If you use conda, please use the following commands:
```shell
conda create --name venv python=3.10
conda activate venv
pip install causaldynamics
```
### Using pdm
Clone the repository and install it using [pdm](https://pdm-project.org/en/latest/):
```shell
git clone https://github.com/kausable/CausalDynamics.git
cd CausalDynamics
pdm install
```
You can test whether the installation succeded by creating some coupled causal model data:
```shell
$(pdm venv activate)
python src/causaldynamics/creator.py --config config.yaml
```
You find the output at `output/` as default location.
## Getting Started
- [Challenge](https://kausable.github.io/CausalDynamics/challenge.html)
- [Quickstart](https://kausable.github.io/CausalDynamics/notebooks/quickstart.html)
- [CausalDynamics](https://kausable.github.io/CausalDynamics/notebooks/causaldynamics.html)
- [Benchmark](https://kausable.github.io/CausalDynamics/benchmark.html)
- [Simple Dynamics](https://kausable.github.io/CausalDynamics/notebooks/simple_causal_models.html)
- [Coupled Dynamics](https://kausable.github.io/CausalDynamics/notebooks/coupled_causal_models.html)
- [Climate Dynamics](https://kausable.github.io/CausalDynamics/notebooks/climate_causal_models.html)
- [Troubleshoot](https://kausable.github.io/CausalDynamics/troubleshoot.html)
## Benchmarking
- [Baseline](https://kausable.github.io/CausalDynamics/baseline.html)
- [Evaluation](https://kausable.github.io/CausalDynamics/notebooks/eval_pipeline.html)
- [Leaderboard](https://kausable.github.io/CausalDynamics/leaderboard.html)
## Citation
If you find any of the code and dataset useful, feel free to acknowledge our work through:
```bibtex
@article{herdeanu2025causaldynamics,
title={CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models},
author={Herdeanu, Benjamin and Nathaniel, Juan and Roesch, Carla and Buch, Jatan and Ramien, Gregor and Haux, Johannes and Gentine, Pierre},
journal={arXiv preprint arXiv:2505.16620},
year={2025}
}
```
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