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        "author": "Iain Barr",
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        "description": "# CausalGraphicalModels\n\n## Introduction\n\n`causalgraphicalmodels` is a python module for describing and manipulating [Causal Graphical Models](https://en.wikipedia.org/wiki/Causal_graph) and [Structural Causal Models](https://en.wikipedia.org/wiki/Structural_equation_modeling). Behind the scenes it is a light wrapper around the python graph library [networkx](https://networkx.github.io/), together with some CGM specific tools.\n\nIt is currently in a very early stage of development. All feedback is welcome.\n\n\n## Example\n\nFor a quick overview of `CausalGraphicalModel`, see [this example notebook](https://github.com/ijmbarr/causalgraphicalmodels/blob/master/notebooks/cgm-examples.ipynb).\n\n## Install\n\n```\npip install causalgraphicalmodels\n```\n\n\n## Resources\nMy understanding of Causality comes mainly from the reading of the follow work:\n - Causality, Pearl, 2009, 2nd Editing. (An overview available [here](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf))\n - A fantastic blog post, [If correlation doesn\u2019t imply causation, then what does?](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/) from Michael Nielsen\n - [These lecture notes](http://www.math.ku.dk/~peters/jonas_files/scriptChapter1-4.pdf) from Jonas Peters\n - The draft of [Elements of Causal Inference](http://www.math.ku.dk/~peters/jonas_files/bookDRAFT5-online-2017-02-27.pdf)\n - http://mlss.tuebingen.mpg.de/2017/speaker_slides/Causality.pdf\n\n## Related Packages\n - [Causality](https://github.com/akelleh/causality)\n - [CausalInference](https://github.com/laurencium/causalinference)\n - [DoWhy](https://github.com/Microsoft/dowhy)\n\n\n\n\n\n",
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