Causalml Python

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.

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, 30/12/2019 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, 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: Python Package for Causal Machine Learning Huigang Chen*, Totte Harinen*, Jeong-Yoon Lee*, Mike Yung*, Zhenyu Zhao* Abstract# CausalML is a Python implementation of algorithms related to causal inference and machine learning.

Algorithms combining causal inference and machine learning have been a trending topic in recent years.

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.

CausalML has 17 repositories available.

Follow their code on GitHub.

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confounding-robust-policy-improvement, To cite CausalML in publications, you can refer to the following sources: 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=.

git clone https://github.

com/uber-common/ causalml.

git cd causalml python setup.

py build_ext –inplace python setup.

py install