Metadata-Version: 2.1
Name: pycausalimpact
Version: 0.0.7
Summary: Python version of Google's Causal Impact model
Home-page: https://github.com/dafiti/causalimpact
Author: Willian Fuks
Author-email: willian.fuks@gmail.com
License: MIT
Description: # Causal Impact [![Build Status](https://travis-ci.com/dafiti/causalimpact.svg?branch=master)](https://travis-ci.com/dafiti/causalimpact) [![Coverage Status](https://coveralls.io/repos/github/dafiti/causalimpact/badge.svg?branch=master)](https://coveralls.io/github/dafiti/causalimpact?branch=master) [![PyPI version](https://badge.fury.io/py/pycausalimpact.svg)](https://badge.fury.io/py/pycausalimpact) [![Pyversions](https://img.shields.io/pypi/pyversions/pycausalimpact.svg)](https://pypi.python.org/pypi/pycausalimpact) [![GitHub license](https://img.shields.io/github/license/dafiti/causalimpact.svg)](https://github.com/dafiti/causalimpact/blob/master/LICENSE)
        
        Python causal impact (or causal inference) implementation of [Google's](https://github.com/google/CausalImpact) model with all functionalities fully ported and tested.
        
        ## How it works
        The main goal of the algorithm is to infer  the expected effect a given intervention (or any action) had on some response variable by analyzing differences between expected and observed time series data.
        
        Data is divided in two parts: the first one is what is known as the "pre-intervention" period and the concept of [Bayesian Structural Time Series](https://en.wikipedia.org/wiki/Bayesian_structural_time_series)  is used to fit a model that best explains what has been observed. The fitted model is used in the second part of data ("post-intervention" period) to forecast what the response would look like had the intervention not taken place. The inferences are based on the differences between observed response to the predicted one which yields the absolute and relative expected effect the intervention caused on data.
        
        The model makes as assumption (which is recommended to be confirmed in your data) that the response variable can be precisely modeled by a linear regression with what is known as "covariates" (or `X`) that **must not** be affected by the intervention that took place (for instance, if a company wants to infer what impact a given marketing campaign will have on its "revenue", then its daily "visits" cannot be used as a covariate as probably the total visits might be affected by the campaign. 
        
        It is more commonly used to infer the impact that marketing interventions have on businesses such as the expected revenue associated to a given campaign or even to assert more precisely the revenue a given channel brings in by completely turning it off (also known as "hold-out" tests). It's important to note though that the model can be extensively used in different areas and subjects; any intervention on time series data can potentially be modeled and inferences be made upon observed and predicted data.
        
        Please refer to <a href=http://nbviewer.jupyter.org/github/dafiti/causalimpact/blob/master/examples/getting_started.ipynb>getting started</a> in the `examples` folder for more information.
        
        ## Instalation
        
            pip install pycausalimpact
        
        ## Requirements
        
         - python{2.7, 3.4, 3.5, 3.6, 3.7}
         - numpy
         - scipy
         - statsmodels
         - matplotlib
         - jinja2
        
        ## Getting Started
        We recommend this [presentation](https://www.youtube.com/watch?v=GTgZfCltMm8) by Kay Brodersen (one of the creators of the causal impact implementation in R).
        
        We also created this introductory [ipython notebook](http://nbviewer.jupyter.org/github/dafiti/causalimpact/blob/master/examples/getting_started.ipynb) with examples of how to use this package.
        
        ### Simple Example
        Here's a simple example (which can also be found in the original Google's R implementation) running in Python:
        
        ```python
        import numpy as np
        import pandas as pd
        from statsmodels.tsa.arima_process import ArmaProcess
        from causalimpact import CausalImpact
        np.random.seed(12345)
        ar = np.r_[1, 0.9]
        ma = np.array([1])
        arma_process = ArmaProcess(ar, ma)
        X = 100 + arma_process.generate_sample(nsample=100)
        y = 1.2 * X + np.random.normal(size=100)
        y[70:] += 5
        data = pd.DataFrame({'y': y, 'X': X}, columns=['y', 'X'])
        pre_period = [0, 69]
        post_period = [70, 99]
        ci = CausalImpact(data, pre_period, post_period)
        print(ci.summary())
        ci.plot()
        ```
        
        ![alt text](https://raw.githubusercontent.com/dafiti/causalimpact/master/examples/ci_plot.png)
        
        ## Contributing, Bugs, Questions
        Contributions are more than welcome! If you want to propose new changes, fix bugs or improve something feel free to fork the repository and send us a Pull Request. You can also open new `Issues` for reporting bugs and general problems.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
Provides-Extra: docs
