Package Health Score

78 / 100

security
No known security issues
popularity
Small
maintenance
Sustainable
community
Active

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Popularity

Small

GitHub Stars: 3.9K
Forks: 271
Contributors: 140

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package causalkit, we found that it has been starred 3,898 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jun 7, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
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License Risk: 0
H
0
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 61
Open PR: 12
Last Release: 6 months ago
Last Commit: 18 days ago

Further analysis of the maintenance status of causalkit based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that causalkit demonstrates a positive version release cadence with at least one new version released in the past 12 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: No
Code of Conduct: Yes
Contributors: 140
Funding: Yes

A good and healthy external contribution signal for causalkit project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.8

Age: 6 months
Latest Release: 6 months ago
Dependencies: N/A
Versions: 2
Maintainers: 1
Wheels: Linux, macOS, Windows

Readme

causalkit: A Rust Package for Causal Inference

causalkit is a rust package that implements a set of algorithms for causal inference modeling. Currently, it supports two tree-based algorithms for individual treatment effect estimation with binary response and continuous response respectively. Given an intervention T and response Y, it estimates the individual treatment effect (ITE) for each sample. Both algorithms support multiple treatments, e.g. for an intervention with K treatments (control, treatment_1, treatment_2, ..., treatment_K), it will output the uplift in treatment effect for each treatment_i against control.

Tree-based algorithms
- Binary Response: Uplift Random Forest on KL-Divergence [1]
- Continuous Response: Uplift Random Forest with honest estimation [2]

This modeling approach can be applied to problems where the personalized effectiveness of an intervention T to target users is concerned.

Installation

It requires python version >=3.8. To use it in python, install as follows

pip install causalkit

You can also download the source code from this github repository and build the python library by yourself via maturin.

Usage

Please check the jupyter notebook in the example folder to learn how to use the library.

References

Literature

Stefan Wager, Susan Athey. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests" arXiv preprint arXiv:1510.04342 (2015).
Susan Athey, Guido Imbens. "Recursive Partitioning for Heterogeneous Causal Effects" arXiv preprint arXiv:1504.01132 (2015).

FAQs

What is causalkit?

. Visit Snyk Advisor to see a full health score report for causalkit, including popularity, security, maintenance & community analysis.

Is causalkit popular?

We’re still processing downloads data for the python package causalkit. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is causalkit well maintained?

We found indications that causalkit maintenance is sustainable demonstrating some project activity. We saw a total of 140 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is causalkit safe to use?

The python package causalkit was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 19 November-2024, at 08:20 (UTC).

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null For more information about how to use this package see README

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Readme

causalkit: A Rust Package for Causal Inference

Installation

Usage

References

Literature

FAQs

What is causalkit?

Is causalkit popular?

Is causalkit well maintained?

Is causalkit safe to use?

Build a secure application checklist

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Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Keep your project healthy

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Readme

causalkit: A Rust Package for Causal Inference

Installation

Usage

References

Literature

FAQs

What is causalkit?

Is causalkit popular?

Is causalkit well maintained?

Is causalkit safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues