{"id":13570208,"url":"https://github.com/ronikobrosly/causal-curve","last_synced_at":"2026-01-18T00:58:42.295Z","repository":{"id":45056259,"uuid":"256017107","full_name":"ronikobrosly/causal-curve","owner":"ronikobrosly","description":"A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.","archived":false,"fork":false,"pushed_at":"2024-05-23T19:17:12.000Z","size":3732,"stargazers_count":277,"open_issues_count":4,"forks_count":18,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-03-16T10:33:52.570Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ronikobrosly.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-04-15T19:30:24.000Z","updated_at":"2025-03-14T17:40:32.000Z","dependencies_parsed_at":"2024-11-05T02:42:44.492Z","dependency_job_id":null,"html_url":"https://github.com/ronikobrosly/causal-curve","commit_stats":{"total_commits":101,"total_committers":3,"mean_commits":"33.666666666666664","dds":0.01980198019801982,"last_synced_commit":"a06fd687a5f746d1cdc5cdef18a65e73230a57d3"},"previous_names":[],"tags_count":35,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronikobrosly%2Fcausal-curve","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronikobrosly%2Fcausal-curve/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronikobrosly%2Fcausal-curve/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronikobrosly%2Fcausal-curve/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ronikobrosly","download_url":"https://codeload.github.com/ronikobrosly/causal-curve/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247134775,"owners_count":20889408,"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":[],"created_at":"2024-08-01T14:00:49.617Z","updated_at":"2026-01-18T00:58:42.282Z","avatar_url":"https://github.com/ronikobrosly.png","language":"Python","funding_links":[],"categories":["Python","Tools","Uncategorized"],"sub_categories":["Causal Inference","Uncategorized"],"readme":"# causal-curve\n\n[![build status](http://img.shields.io/travis/ronikobrosly/causal-curve/main.svg?style=flat)](https://travis-ci.org/ronikobrosly/causal-curve)\n[![codecov](https://codecov.io/gh/ronikobrosly/causal-curve/branch/main/graph/badge.svg)](https://codecov.io/gh/ronikobrosly/causal-curve)\n[![DOI](https://zenodo.org/badge/256017107.svg)](https://zenodo.org/badge/latestdoi/256017107)\n\nPython tools to perform causal inference when the treatment of interest is continuous.\n\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"/imgs/curves.png\" align=\"middle\"/\u003e\n\u003c/p\u003e\n\n\n## Currently getting a makeover. Refactor and modernization is happening:\n\n* Packaging \u0026 Build System: Updating to run in python 3.13+, updating to latest PEP build standards\n* CI/CD: Shifting away from Travis CI to GitHub Actions\n* Updating how the dependencies are used: `pandas` and `NumPy` have undergone major, breaking updates\n* Switching to `myst-parser` to move away from RST documentation\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Installation](#installation)\n- [Documentation](#documentation)\n- [Contributing](#contributing)\n- [Citation](#citation)\n- [References](#references)\n\n## Overview\n\n(**Version 1.0.0 released in January 2021!**)\n\nThere are many implemented methods to perform causal inference when your intervention of interest is binary,\nbut few methods exist to handle continuous treatments.\n\nThis is unfortunate because there are many scenarios (in industry and research) where these methods would be useful.\nFor example, when you would like to:\n\n* Estimate the causal response to increasing or decreasing the price of a product across a wide range.\n* Understand how the number of minutes per week of aerobic exercise causes positive health outcomes.\n* Estimate how decreasing order wait time will impact customer satisfaction, after controlling for confounding effects.\n* Estimate how changing neighborhood income inequality (Gini index) could be causally related to neighborhood crime rate.\n\nThis library attempts to address this gap, providing tools to estimate causal curves (AKA causal dose-response curves).\nBoth continuous and binary outcomes can be modeled against a continuous treatment.\n\n## Installation\n\nAvailable via PyPI:\n\n`pip install causal-curve`\n\nYou can also get the latest version of causal-curve by cloning the repository::\n\n```\ngit clone -b main https://github.com/ronikobrosly/causal-curve.git\ncd causal-curve\npip install .\n```\n\n## Documentation\n\n[Documentation, tutorials, and examples are available at readthedocs.org](https://causal-curve.readthedocs.io/en/latest/)\n\n\n## Contributing\n\nYour help is absolutely welcome! Please do reach out or create a feature branch!\n\n## Citation\n\nKobrosly, R. W., (2020). causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves. Journal of Open Source Software, 5(52), 2523, [https://doi.org/10.21105/joss.02523](https://doi.org/10.21105/joss.02523)\n\n## References\n\nGalagate, D. Causal Inference with a Continuous Treatment and Outcome: Alternative\nEstimators for Parametric Dose-Response function with Applications. PhD thesis, 2016.\n\nHirano K and Imbens GW. The propensity score with continuous treatments.\nIn: Gelman A and Meng XL (eds) Applied bayesian modeling and causal inference\nfrom incomplete-data perspectives. Oxford, UK: Wiley, 2004, pp.73–84.\n\nImai K, Keele L, Tingley D. A General Approach to Causal Mediation Analysis. Psychological\nMethods. 15(4), 2010, pp.309–334.\n\nKennedy EH, Ma Z, McHugh MD, Small DS. Nonparametric methods for doubly robust estimation\nof continuous treatment effects. Journal of the Royal Statistical Society, Series B. 79(4), 2017, pp.1229-1245.\n\nMoodie E and Stephens DA. Estimation of dose–response functions for\nlongitudinal data using the generalised propensity score. In: Statistical Methods in\nMedical Research 21(2), 2010, pp.149–166.\n\nvan der Laan MJ and Gruber S. Collaborative double robust penalized targeted\nmaximum likelihood estimation. In: The International Journal of Biostatistics 6(1), 2010.\n\nvan der Laan MJ and Rubin D. Targeted maximum likelihood learning. In: ​U.C. Berkeley Division of\nBiostatistics Working Paper Series, 2006.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronikobrosly%2Fcausal-curve","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fronikobrosly%2Fcausal-curve","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronikobrosly%2Fcausal-curve/lists"}