Beat-ML1

v0.13.1

This package contains several methods for calculating Conditional Average Treatment Effects For more information about how to use this package see README

Latest version published 2 years ago

License: MIT

PyPI

GitHub

Package Health Score

52 / 100

security
No known security issues
popularity
Small
maintenance
Inactive
community
Sustainable

Popularity

Small

GitHub Stars: 3.63K
Forks: 687
Contributors: 40

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package Beat-ML1, we found that it has been starred 3,632 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 24, 2022
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
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0
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0
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License Risk: 0
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0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 332
Open PR: 21
Last Release: 2 years ago
Last Commit: 5 months ago

Further analysis of the maintenance status of Beat-ML1 based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for Beat-ML1 is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 40
Funding: No

With more than 10 contributors for the Beat-ML1 repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like Beat-ML1 is missing a Code of Conduct.

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Package

Python Versions Compatibility: ==3.6.*, ==3.7.*, ==3.8.*, ==3.9.*

Age: 2 years
Latest Release: 2 years ago
Dependencies: 11 Direct
Versions: 1
Maintainers: 1
Wheels: macOS

Readme

BEAT: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation

BEAT is a Python package for estimating heterogeneous treatment effects from observational data via machine learning:

All arguments are the same as the original package, but there are two new inputs: target.weight.penalty indicates the penalty assigned to the protected attributes. target.weights is a matrix that includes the protected characteristics. X should not inlcude the protected characteristics.
See full details about the BEAT method in the original paper: Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT)
Forked from https://github.com/Microsoft/EconML

Getting Started

Installation

Install the latest release from PyPI:

pip install BEAT_TEST

Usage Examples

Estimation Methods

from econml.grf import BeatForest
#Setting Training treatment and outcome 
treatment = ['W']
outcome = ['Y']
Y = train[outcome]
T = train[treatment]
#Setting Unprotected variables
unprotected_covariate = ['X.V1', 'X.V2', 'X.V3', 'X.V4', 'X.V5', 'Z.V1', 'Z.V2', 'Z.V3', 'Z.V4']
X1 = train[unprotected_covariate]
#set parameters for BEAT and Fit in training values
BEAT = BeatForest(alpha = 10, demean = 0, n_estimators = 8)                     
BEAT.fit(X1, T, Y) 
#Get prediction from test dataset
prediction = BEAT.predict(X_test)

References

Ascarza, E., & Israeli, A. (2022). Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (beat). Proceedings of the National Academy of Sciences, 119(11). https://doi.org/10.1073/pnas.2115293119

Beat-ML1 dependencies

joblib keras lightgbm numpy pandas scikit-learn scipy shap sparse statsmodels tensorflow

FAQs

What is Beat-ML1?

This package contains several methods for calculating Conditional Average Treatment Effects. Visit Snyk Advisor to see a full health score report for Beat-ML1, including popularity, security, maintenance & community analysis.

Is Beat-ML1 popular?

The python package Beat-ML1 receives a total of 37 weekly downloads. As such, Beat-ML1 popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is Beat-ML1 well maintained?

We found indications that Beat-ML1 is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is Beat-ML1 safe to use?

The python package Beat-ML1 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 25 September-2024, at 16:36 (UTC).

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