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        "description": "Causal Inference in Python\n==========================\n\n*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.\n\nWork on *Causalinference* started in 2014 by Laurence Wong as a personal side project. It is distributed under the 3-Clause BSD license.\n\nImportant Links\n===============\n\nThe official website for *Causalinference* is\n\n  http://causalinferenceinpython.org\n\nThe most current development version is hosted on GitHub at\n\n  https://github.com/laurencium/causalinference\n\nPackage source and binary distribution files are available from PyPi at\n\n  https://pypi.python.org/pypi/causalinference\n\nFor an overview of the main features and uses of *Causalinference*, please refer to\n\n  https://github.com/laurencium/causalinference/blob/master/docs/tex/vignette.pdf\n\nA blog dedicated to providing a more detailed walkthrough of *Causalinference* and the econometric theory behind it can be found at\n\n  http://laurence-wong.com/software/\n\nMain Features\n=============\n\n* Assessment of overlap in covariate distributions\n* Estimation of propensity score\n* Improvement of covariate balance through trimming\n* Subclassification on propensity score\n* Estimation of treatment effects via matching, blocking, weighting, and least squares\n\nDependencies\n============\n\n* NumPy: 1.8.2 or higher\n* SciPy: 0.13.3 or higher\n\nInstallation\n============\n\n*Causalinference* can be installed using ``pip``: ::\n\n  $ pip install causalinference\n\nFor help on setting up Pip, NumPy, and SciPy on Macs, check out this excellent `guide <http://www.sourabhbajaj.com/mac-setup>`_.\n\nMinimal Example\n===============\n\nThe following illustrates how to create an instance of CausalModel: ::\n\n  >>> from causalinference import CausalModel\n  >>> from causalinference.utils import random_data\n  >>> Y, D, X = random_data()\n  >>> causal = CausalModel(Y, D, X)\n\nInvoking ``help`` on ``causal`` at this point should return a comprehensive listing of all the causal analysis tools available in *Causalinference*.",
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