Metadata-Version: 1.0
Name: CausalInference
Version: 0.1.2
Summary: Causal Inference in Python
Home-page: https://github.com/laurencium/causalinference
Author: Laurence Wong
Author-email: laurencium@gmail.com
License: LICENSE.txt
Description: Causal Inference in Python
        ==========================
        
        *Causal Inference in Python*, or *Causalinference* in short, is a software package that implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis.
        
        Work on *Causalinference* started in 2014 by Laurence Wong as a personal side project. It is distributed under the 3-Clause BSD license.
        
        Important Links
        ===============
        
        The official website for *Causalinference* is
        
          http://causalinferenceinpython.org
        
        The most current development version is hosted on GitHub at
        
          https://github.com/laurencium/causalinference
        
        Package source and binary distribution files are available from PyPi at
        
          https://pypi.python.org/pypi/causalinference
        
        For an overview of the main features and uses of *Causalinference*, please refer to
        
          https://github.com/laurencium/causalinference/blob/master/docs/tex/vignette.pdf
        
        A blog dedicated to providing a more detailed walkthrough of *Causalinference* and the econometric theory behind it can be found at
        
          http://laurence-wong.com/software/
        
        Main Features
        =============
        
        * Assessment of overlap in covariate distributions
        * Estimation of propensity score
        * Improvement of covariate balance through trimming
        * Subclassification on propensity score
        * Estimation of treatment effects via matching, blocking, weighting, and least squares
        
        Dependencies
        ============
        
        * NumPy: 1.8.2 or higher
        * SciPy: 0.13.3 or higher
        
        Installation
        ============
        
        *Causalinference* can be installed using ``pip``: ::
        
          $ pip install causalinference
        
        For help on setting up Pip, NumPy, and SciPy on Macs, check out this excellent `guide <http://www.sourabhbajaj.com/mac-setup>`_.
        
        Minimal Example
        ===============
        
        The following illustrates how to create an instance of CausalModel: ::
        
          >>> from causalinference import CausalModel
          >>> from causalinference.utils import random_data
          >>> Y, D, X = random_data()
          >>> causal = CausalModel(Y, D, X)
        
        Invoking ``help`` on ``causal`` at this point should return a comprehensive listing of all the causal analysis tools available in *Causalinference*.
        
        
Platform: UNKNOWN
