Metadata-Version: 2.1
Name: causalimpactreturnsummary
Version: 1.0.0
Summary: Python Package for causal inference using Bayesian structural time-series models with data returned..
Home-page: https://github.com/jamalsenouci/causalimpact/
Author: Dan Wilkinson
Author-email: danwdigital@gmail.com
License: MIT
Project-URL: Documentation, https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb
Project-URL: Source, https://github.com/jamalsenouci/causalimpact/
Project-URL: Changelog, https://github.com/jamalsenouci/causalimpact/CHANGELOG.md
Project-URL: Download, https://pypi.python.org/pypi/causalimpact/
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=2.6
Description-Content-Type: text/markdown; charset=UTF-8
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: statsmodels
Requires-Dist: matplotlib
Requires-Dist: pymc
Requires-Dist: pytensor
Requires-Dist: importlib-metadata; python_version < "3.8"
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"

## CausalImpact

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![monthly downloads](https://pepy.tech/badge/causalimpact/month)
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#### A Python package for causal inference using Bayesian structural time-series models

This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.

This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.

As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.

#### Try it out in the browser

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb)

#### Installation

install the latest release via pip

```bash
pip install causalimpact
```

#### Getting started

[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb)

#### Further resources

- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)

#### Bugs

The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.
