Skip to main content

This package contains several methods for calculating Conditional Average Treatment Effects

Project description

Introduction

The ALICE project at Microsoft Research is aimed at applying Artificial Intelligence concepts to economic decision making. The Microsoft EconML pacakge is part of that project, providing a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x. For more information about how to use this package, consult the documentation at https://econml.azurewebsites.net/.

Getting Started

For developers, you can get starting by cloning this repository. We use setuptools for building and distributing our package. We rely on some recent features of setuptools, so make sure to upgrade to a recent version with pip install setuptools --upgrade. Then from your local copy of the repository you can run python setup.py develop to get started.

Running the tests

This project uses pytest for testing. To run tests locally after installing the package, you can use python setup.py pytest.

Generating the documentation

This project's documentation is generated via Sphinx. To generate a local copy of the documentation from a clone of this repository, just run python setup.py build_sphinx, which will build the documentation and place it under the build/sphinx/html path.

The reStructuredText files that make up the documentation are stored in the docs directory; module documentation is automatically generated by the Sphinx build process.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

econml-0.1.tar.gz (162.1 kB view hashes)

Uploaded Source

Built Distributions

econml-0.1-py3.6.egg (263.9 kB view hashes)

Uploaded Source

econml-0.1-py3-none-any.whl (178.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page