{"id":9243721,"url":"https://github.com/wayfair/pylift","last_synced_at":"2025-08-17T08:32:53.029Z","repository":{"id":50490153,"uuid":"149136560","full_name":"wayfair/pylift","owner":"wayfair","description":"Uplift modeling package.","archived":true,"fork":false,"pushed_at":"2022-10-28T15:21:53.000Z","size":7840,"stargazers_count":368,"open_issues_count":16,"forks_count":75,"subscribers_count":21,"default_branch":"master","last_synced_at":"2024-03-25T20:23:12.276Z","etag":null,"topics":["hacktoberfest"],"latest_commit_sha":null,"homepage":"http://pylift.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wayfair.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"docs/contributing.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-09-17T14:17:57.000Z","updated_at":"2024-03-18T06:41:15.000Z","dependencies_parsed_at":"2023-01-20T19:05:11.050Z","dependency_job_id":null,"html_url":"https://github.com/wayfair/pylift","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wayfair%2Fpylift","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wayfair%2Fpylift/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wayfair%2Fpylift/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wayfair%2Fpylift/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wayfair","download_url":"https://codeload.github.com/wayfair/pylift/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":216818638,"owners_count":16083832,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["hacktoberfest"],"created_at":"2024-05-08T00:10:56.817Z","updated_at":"2024-08-24T22:30:41.528Z","avatar_url":"https://github.com/wayfair.png","language":"Python","funding_links":[],"categories":["Causal Inference","Tools"],"sub_categories":["Others","Uplift Evaluation"],"readme":"# pylift\n\n[![Documentation Status](https://readthedocs.org/projects/pylift/badge/?version=latest)](https://pylift.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.com/rsyi/pylift.svg?branch=master)](https://travis-ci.com/rsyi/pylift)\n\n[Read our documentation!](https://pylift.readthedocs.io/en/latest/)\n\n**pylift** is an uplift library that provides, primarily, (1) fast uplift\nmodeling implementations and (2) evaluation tools. While other packages and\nmore exact methods exist to model uplift, **pylift** is designed to be quick,\nflexible, and effective. **pylift** heavily leverages the optimizations of\nother packages -- namely, `xgboost`, `sklearn`, `pandas`, `matplotlib`,\n`numpy`, and `scipy`. The primary method currently implemented is the\nTransformed Outcome proxy method (Athey 2015).\n\n## License\nLicensed under the BSD-2-Clause by the authors.\n\n## Reference\nAthey, S., \u0026 Imbens, G. W. (2015). Machine learning methods for estimating\nheterogeneous causal effects. stat, 1050(5).\n\nGutierrez, P., \u0026 Gérardy, J. Y. (2017). Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1-13).\n\nHitsch, G., \u0026 Misra, S. (2018). Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation. Preprint\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwayfair%2Fpylift","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwayfair%2Fpylift","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwayfair%2Fpylift/lists"}