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Summary: Spatio Temporal Causal Benchmarking Platform
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Keywords: benchmark,causal,causalbench,causality,inference
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Description-Content-Type: text/markdown

# CausalBench

The up-to-date documentation regarding usage and features of CausalBench can be found at [https://docs.causalbench.org](https://docs.causalbench.org).

Registration at [CausalBench website](https://causalbench.org) is required in order to utilize the CausalBench package.
### Install CausalBench: 
`pip install causalbench-asu`

## Overview

CausalBench is a flexible, fair, and easy-to-use evaluation platform designed to advance research in causal learning. It facilitates scientific collaboration by providing a suite of tools for novel algorithms, datasets, and metrics. Our mission is to promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. CausalBench serves as a comprehensive benchmarking resource, impacting a broad range of scientific and engineering disciplines.

## Features

-   **Transparent and Fair Evaluation**: Ensures unbiased and transparent benchmarking processes.
-   **Facilitation of Scientific Collaboration**: Encourages the sharing and development of novel algorithms, datasets, and metrics.
-   **Scientific Objectivity**: Promotes objective assessment and comparison of causal learning methods.
-   **Reproducibility**: Supports the reproducibility of research results, a cornerstone of scientific integrity.
-   **Awareness of Bias**: Highlights and addresses biases in causal learning research.

## Services Provided

1.  **Benchmarking Data**: A repository of diverse datasets specifically curated for evaluating causal learning algorithms.
2.  **Algorithm Evaluation**: Tools and frameworks for testing and comparing the performance of causal learning algorithms.
3.  **Model Benchmarking**: Standards and protocols for assessing the efficacy of different causal models.
4.  **Metric Evaluation**: A collection of metrics tailored for comprehensive evaluation of causal learning techniques.

## Impact

CausalBench meets the needs of various scientific and engineering disciplines by providing essential resources and standards for evaluating causal learning methods. This platform helps researchers to:

-   Collaborate and share advancements in causal learning.
-   Ensure their work meets high standards of scientific rigor and fairness.
-   Access a centralized repository of resources for benchmarking and evaluation.

## Getting Started

To start using CausalBench, follow these steps:

1.  **Installation**: Instructions for installing CausalBench can be found [here](https://docs.causalbench.org/install/).
2.  **Documentation**: Comprehensive documentation for CausalBench, including CausalBench terms and tutorials, is available [here](https://docs.causalbench.org/).

## Contributing

CausalBench is an open-source project and welcomes contributions from the community. We plan to announce the contribution guideline soon. 

## License

CausalBench is licensed under the Apache License.

## Contact

For questions, feedback, or further information, please contact us at support@causalbench.org.

## Acknowledgments
This work is supported by NSF grant 2311716, "CausalBench: A Cyberinfrastructure
for Causal-Learning Benchmarking for Efficacy, Reproducibility, and Scientific
Collaboration".

## Support Benchmark Context
CausalBench is structured to support different machine learning tasks and dataset types. With user contribution, the supported context will be expanded, currently (as of 8/12/25), these models and tasks are provided.    

| Dataset               | File                 | Description                                                                                                                                           |
|-----------------------|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| Abalone               | data, static graph   |                                                                                                                                                       |
| Adult                 | data, static graph   |                                                                                                                                                       |
| Sachs                 | data, static graph   |                                                                                                                                                       |
| NetSim                | data, static graph   | Brain FMRI scan<br/> - 28 simulations <br/> - Each has different DGPs, num of nodes (5, 50), num of observations (50 to 5000), 1400 datasets in total |
| Time series simulated | data, temporal graph |                                                                                                                                                       |
| Telecom               | data, temporal graph |                                                                                                                                                       |


| Model      | Task     |
|------------|----------|
| PC         | Static   |
| GES        | Static   |
| VAR-LiNGAM | Temporal |
| PCMCIplus  | Temporal |

| Metric    | Task     |
|-----------|----------|
| Accuracy  | Static   |
| F1        | Static   |
| Precision | Static   |
| Recall    | Static   |
| SHD       | Static   |
| Accuracy  | Temporal |
| F1        | Temporal |
| Precision | Temporal |
| Recall    | Temporal |
| SHD       | Temporal |
