Home » Luggage » Causalml Uber

Causalml Uber

30/12/2019 uber / causalml.

Watch 41 Star 1.

1k Fork 166 Uplift modeling and causal inference with machine learning algorithms View license 1.

1k stars 166 forks Star Watch Code; Issues 19; Pull requests 4; Actions; Projects 1; Security 1; Insights Dismiss Join GitHub today.

GitHub is home to over 50 million developers working together to host and review.

git clone https://github.

com/ uber -common/ causalml.

git cd causalml python setup.

py build_ext –inplace python setup.

py install, Whitepaper: CausalML : Python Package for Causal Machine Learning.

Bibtex: @misc{chen2020causalml, title={ CausalML : Python Package for Causal Machine Learning}, author={Huigang Chen and Totte Harinen and Jeong-Yoon Lee and Mike Yung and Zhenyu Zhao}, year={2020}, eprint={2002.

11631}, archivePrefix={arXiv}, primaryClass={cs.

CY} } Papers, ” Causalml ” and other potentially trademarked words, copyrighted images and copyrighted readme contents likely belong to the legal entity who owns the ” Uber ” organization.

Awesome Open Source is not affi.

liated with the legal entity who owns the ” Uber ” organization.

uber / causalml.

Watch 35 Fork 119 Code.

Issues 13.

Pull requests 2.

Actions Projects 1.

Security Insights Code.

Issues 13.

Pull requests 2.

Projects 1.

Actions.

Security.

Pulse Labels 14 Milestones 0 Labels 14 Milestones 0 New issue Have a question about this project?

.

05/12/2019 Dismiss Join GitHub today.

GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

It was because the notebook imported causalml from the source code directory even when causalml has been installed in the system.

However, importing from the source code directory requires compiled Cython objects – as you noted, with python setup.

py build_ext –inplace.

19/09/2019 uber / causalml.

Watch 34 Star 822 Fork 105 Code.

Issues 10.

Pull requests 1.

Actions Projects 1.

Security Insights Code.

Issues 10.

Pull requests 1.

Projects 1.

Actions.

Security.

Pulse Labels 14 Milestones 0 Labels 14 Milestones 0 New pull request New.

1 Open 84 Closed 1 Open.

Meta-Learner Algorithms.

A meta-algorithm (or meta-learner) is a framework to estimate the Conditional Average Treatment Effect (CATE) using any machine learning estimators (called base learners) [kunzel2019metalearners].

A meta-algorithm uses either a single base learner while having the treatment indicator as a feature (e.

g.

S-learner), or multiple base learners separately for each of the.

About Causal ML .

Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research.

It provides a standard interface that allows user to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data.

causalml uber