causal-curve
causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves - Published in JOSS (2020)
Science Score: 93.0%
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Published in Journal of Open Source Software
Scientific Fields
Repository
A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.
Basic Info
Statistics
- Stars: 279
- Watchers: 6
- Forks: 18
- Open Issues: 4
- Releases: 35
Metadata Files
README.md
causal-curve
Python tools to perform causal inference when the treatment of interest is continuous.
Table of Contents
Overview
(Version 1.0.0 released in January 2021!)
There are many implemented methods to perform causal inference when your intervention of interest is binary, but few methods exist to handle continuous treatments.
This is unfortunate because there are many scenarios (in industry and research) where these methods would be useful. For example, when you would like to:
- Estimate the causal response to increasing or decreasing the price of a product across a wide range.
- Understand how the number of minutes per week of aerobic exercise causes positive health outcomes.
- Estimate how decreasing order wait time will impact customer satisfaction, after controlling for confounding effects.
- Estimate how changing neighborhood income inequality (Gini index) could be causally related to neighborhood crime rate.
This library attempts to address this gap, providing tools to estimate causal curves (AKA causal dose-response curves). Both continuous and binary outcomes can be modeled against a continuous treatment.
Installation
Available via PyPI:
pip install causal-curve
You can also get the latest version of causal-curve by cloning the repository::
git clone -b main https://github.com/ronikobrosly/causal-curve.git
cd causal-curve
pip install .
Documentation
Documentation, tutorials, and examples are available at readthedocs.org
Contributing
Your help is absolutely welcome! Please do reach out or create a feature branch!
Citation
Kobrosly, 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
References
Galagate, D. Causal Inference with a Continuous Treatment and Outcome: Alternative Estimators for Parametric Dose-Response function with Applications. PhD thesis, 2016.
Hirano K and Imbens GW. The propensity score with continuous treatments. In: Gelman A and Meng XL (eds) Applied bayesian modeling and causal inference from incomplete-data perspectives. Oxford, UK: Wiley, 2004, pp.73–84.
Imai K, Keele L, Tingley D. A General Approach to Causal Mediation Analysis. Psychological Methods. 15(4), 2010, pp.309–334.
Kennedy EH, Ma Z, McHugh MD, Small DS. Nonparametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society, Series B. 79(4), 2017, pp.1229-1245.
Moodie E and Stephens DA. Estimation of dose–response functions for longitudinal data using the generalised propensity score. In: Statistical Methods in Medical Research 21(2), 2010, pp.149–166.
van der Laan MJ and Gruber S. Collaborative double robust penalized targeted maximum likelihood estimation. In: The International Journal of Biostatistics 6(1), 2010.
van der Laan MJ and Rubin D. Targeted maximum likelihood learning. In: U.C. Berkeley Division of Biostatistics Working Paper Series, 2006.
Owner
- Name: Roni Kobrosly
- Login: ronikobrosly
- Kind: user
- Location: Washington DC
- Company: DrFirst
- Website: https://www.kobrosly.net
- Repositories: 8
- Profile: https://github.com/ronikobrosly
data person 💻
JOSS Publication
causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves
Authors
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Flowcast, 44 Tehama St, San Francisco, CA, USA
Editor
Olivia Guest
Tags
causal inference causality machine learningGitHub Events
Total
- Watch event: 6
Last Year
- Watch event: 6
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Roni Kobrosly | r****y@g****m | 99 |
| dirk | d****r@g****m | 2 |
| rkobrosly | r****y@d****m | 1 |
| Roni Kobrosly | r****y@R****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 25
- Total pull requests: 23
- Average time to close issues: 21 days
- Average time to close pull requests: about 5 hours
- Total issue authors: 10
- Total pull request authors: 2
- Average comments per issue: 2.52
- Average comments per pull request: 0.61
- Merged pull requests: 23
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: 3 days
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 3.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- ronikobrosly (13)
- athammad (2)
- nsankar (2)
- v6l4188 (2)
- juandavidgutier (1)
- dirknbr (1)
- AlexMa011 (1)
- alexjonesphd (1)
- NiklasTR (1)
- gabeschulman (1)
Pull Request Authors
- ronikobrosly (21)
- dirknbr (4)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 7,487 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 2
(may contain duplicates) - Total versions: 50
- Total maintainers: 1
proxy.golang.org: github.com/ronikobrosly/causal-curve
- Documentation: https://pkg.go.dev/github.com/ronikobrosly/causal-curve#section-documentation
- License: mit
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Latest release: v1.0.7
published over 1 year ago
Rankings
pypi.org: causal-curve
A python library with tools to perform causal inference using observational data when the treatment of interest is continuous.
- Homepage: https://github.com/ronikobrosly/causal-curve
- Documentation: https://causal-curve.readthedocs.io/
- License: MIT License
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Latest release: 1.0.6
published over 4 years ago
Rankings
Maintainers (1)
Dependencies
- black *
- coverage *
- future *
- joblib *
- numpy *
- numpydoc *
- pandas *
- patsy *
- progressbar2 *
- pygam *
- pytest *
- python-dateutil *
- python-utils *
- pytz *
- scikit-learn *
- scipy *
- six *
- sphinx_rtd_theme *
- statsmodels *
- black *
- coverage *
- future *
- joblib *
- numpy *
- numpydoc *
- pandas *
- patsy *
- progressbar2 *
- pygam *
- pytest *
- python-dateutil *
- python-utils *
- pytz *
- scikit-learn *
- scipy *
- six *
- sphinx_rtd_theme *
- statsmodels *
- black *
- coverage *
- future *
- joblib *
- numpy *
- numpydoc *
- pandas *
- patsy *
- progressbar2 *
- pygam *
- pytest *
- python-dateutil *
- python-utils *
- pytz *
- scikit-learn *
- scipy *
- six *
- sphinx_rtd_theme *
- statsmodels *