Metadata-Version: 2.1
Name: causal-curve
Version: 0.5.2
Summary: A python library with tools to perform causal inference using         observational data when the treatment of interest is continuous.
Home-page: https://github.com/ronikobrosly/causal-curve
Author: Roni Kobrosly
Author-email: roni.kobrosly@gmail.com
License: UNKNOWN
Description: # causal-curve
        
        [![build status](http://img.shields.io/travis/ronikobrosly/causal-curve/master.svg?style=flat)](https://travis-ci.org/ronikobrosly/causal-curve)
        [![codecov](https://codecov.io/gh/ronikobrosly/causal-curve/branch/master/graph/badge.svg)](https://codecov.io/gh/ronikobrosly/causal-curve)
        [![DOI](https://zenodo.org/badge/256017107.svg)](https://zenodo.org/badge/latestdoi/256017107)
        
        Python tools to perform causal inference using observational data when the treatment of interest is continuous.
        
        
        <p align="center">
        <img src="https://upload.wikimedia.org/wikipedia/commons/e/e8/Antikythera_mechanism.svg" align="middle" width="350" height="477" />
        </p>
        
        
        [The Antikythera mechanism](https://en.wikipedia.org/wiki/Antikythera_mechanism), an ancient analog computer, with lots of beautiful curves.
        
        
        
        ## Table of Contents
        
        - [Overview](#overview)
        - [Installation](#installation)
        - [Documentation](#documentation)
        - [In Progress](#in-progress)
        - [Contributing](#contributing)
        - [Citation](#citation)
        - [References](#references)
        
        ## Overview
        
        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 master https://github.com/ronikobrosly/causal-curve.git
        cd causal-curve
        pip install .
        ```
        
        ## Documentation
        
        [Documentation is available at readthedocs.org](https://causal-curve.readthedocs.io/en/latest/)
        
        ## 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](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.
        
        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.
        
        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.
        
        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.
        
        Imai K., Keele L., Tingley D. A General Approach to Causal Mediation Analysis. Psychological
        Methods. 15(4), 2010, pp.309–334.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
