{
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        "author": "IBM Research Haifa Labs - Machine Learning for Healthcare and Life Sciences",
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        "description": "[![Build Status](https://travis-ci.org/IBM/causallib.svg?&branch=master)](https://travis-ci.org/IBM/causallib)\n[![Test Coverage](https://api.codeclimate.com/v1/badges/db2562e44c4a9f7280dc/test_coverage)](https://codeclimate.com/github/IBM/causallib/test_coverage)\n[![PyPI version](https://badge.fury.io/py/causallib.svg)](https://badge.fury.io/py/causallib)\n[![Documentation Status](https://readthedocs.org/projects/causallib/badge/?version=latest)](https://causallib.readthedocs.io/en/latest/)\n# IBM Causal Inference Library\nA Python package for computational inference of causal effect.\n\n## Description\nCausal inference analysis allows estimating of the effect of intervention\non some outcome from observational data.\nIt deals with the selection bias that is inherent to such data.  \n\nThis python package allows creating modular causal inference models\nthat internally utilize machine learning models of choice,\nand can estimate either individual or average outcome given an intervention.\nThe package also provides the means to evaluate the performance of the \nmachine learning models and their predictions.\n\nThe machine learning models must comply with scikit-learn's api \nand contain `fit()` and `predict()` functions. \nCategorical models must also implement `predict_proba()`. \n\n## Installation\n```bash\npip install causallib\n```\n\n## Usage\nIn general, the package is imported using the name `causallib`. \nFor example, use \n```Python\nfrom sklearn.linear_model import LogisticRegression\nfrom causallib.estimation import IPW \nipw = IPW(LogisticRegression())\n```\nComprehensive Jupyter Notebooks examples can be found in the [examples directory](examples).\n\n\n\n",
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        "name": "causallib",
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