Datasets:

kausable
/

CausalDynamics

@@ -16,8 +16,6 @@ license: mit
 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
@@ -26,22 +24,38 @@ A comprehensive benchmark framework designed to rigorously evaluate state-of-the
 **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.
 ## Installation
-The easiest way to install the package is via PyPi:
-```bash
-pip install causaldynamics
 ```
-Although you can generate your own dataset (see [getting started](#getting-started)), you can download our preprocessed ones directly from HuggingFace:
-```bash
-wget https://huggingface.co/datasets/kausable/CausalDynamics/resolve/main/process_causaldynamics.py
-python process_causaldynamics.py
 ```
-See the [additional installation guide](#additional-installation-guide) for more options.
 ## Getting Started
@@ -59,21 +73,5 @@ See the [additional installation guide](#additional-installation-guide) for more
 - [Evaluation](https://kausable.github.io/CausalDynamics/notebooks/eval_pipeline.html)
 - [Leaderboard](https://kausable.github.io/CausalDynamics/leaderboard.html)
-## Additional Installation Guide
-Note: This is the recommended way if you want to run scripts to generate benchmark data. Clone the repository and install it using [pdm](https://pdm-project.org/en/latest/):
-```shell
-git clone https://github.com/kausable/CausalDynamics.git
-pdm install
-```
-You can test whether the installation succeded by creating some coupled causal model data:
-```shell
-python src/causaldynamics/creator.py --config config.yaml
-```
-You find the output at `output/<timestamp>` as default location.
 ## Citation
 If you find any of the code and dataset useful, feel free to acknowledge our work through:

 A comprehensive benchmark framework designed to rigorously evaluate state-of-the-art causal discovery algorithms for dynamical systems.
 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
 **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.
+## Preprocessed 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 pdm
+Clone the repository and install it using [pdm](https://pdm-project.org/en/latest/):
+```shell
+git clone https://github.com/kausable/CausalDynamics.git
+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/<timestamp>` as default location.
+### Using pip
+Alternatively, `causaldynamics` is available on [PyPi](https://pypi.org/project/causaldynamics/), so you can use pip to install `causaldynamcis`, which currently requires Python version `3.10`.
+```bash
+pip install causaldynamics
+```
 ## Getting Started
 - [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: