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Research
Data Theft Repackaged: A Case Study in Malicious Wrapper Packages on npm
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
The up-to-date documentation regarding usage and features of CausalBench can be found at https://docs.causalbench.org.
Registration at CausalBench website is required in order to utilize the CausalBench package.
pip install causalbench-asu
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.
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:
To start using CausalBench, follow these steps:
CausalBench is an open-source project and welcomes contributions from the community. We plan to announce the contribution guideline soon.
CausalBench is licensed under the Apache License.
For questions, feedback, or further information, please contact us at support@causalbench.org.
This work is supported by NSF grant 2311716, "CausalBench: A Cyberinfrastructure for Causal-Learning Benchmarking for Efficacy, Reproducibility, and Scientific Collaboration".
CausalBench is structured to support different machine learning tasks and dataset types. With user contribution, the supported context will be expanded, currently, 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 - 28 simulations - 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 |
FAQs
Spatio Temporal Causal Benchmarking Platform
We found that causalbench-asu demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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