A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.
Python tools to perform causal inference when the treatment of interest is continuous.
(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:
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.
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 .
Your help is absolutely welcome! Please do reach out or create a feature branch!
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
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