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README.rst

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@@ -96,7 +96,7 @@ DoWhy stresses on the interpretability of its output. At any point in the analys
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you can inspect the untested assumptions, identified estimands (if any) and the
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estimate (if any). Here's a sample output of the linear regression estimator.
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.. image:: docs/images/regression_output.png
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.. image:: /docs/images/regression_output.png
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For detailed code examples, check out the Jupyter notebooks in `docs/source/ <docs/source/>`_, or try them online at `Binder <https://mybinder.org/v2/gh/microsoft/dowhy/master?filepath=docs%2Fsource%2F>`_.
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outcome='y',
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common_causes=['X0']).groupby('v').mean().plot(y='y', kind='bar')
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.. image:: docs/images/do_barplot.png
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.. image:: /docs/images/do_barplot.png
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The :code:`do` method in the causal namespace generates a random sample from $P(outcome|do(X=x))$ of the
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same length as your data set, and returns this outcome as a new :code:`DataFrame`. You can continue to perform

docs/Makefile

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#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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@echo "pdflatex finished; see $(PDF)"
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commithtmldocs: html latexpdf
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commithtmldocs: html
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cd $(BUILDDIR)/html; git add . ; git commit -m "rebuilt docs"; git push origin gh-pages
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# Catch-all target: route all unknown targets to Sphinx using the new

docs/source/code_repo.rst

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You can browse the code in a html-friendly format `here
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<https://github.com/Microsoft/dowhy>`_.
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Installation
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------------
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**Requirements**
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DoWhy support Python 3+. It requires the following packages:
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* numpy
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* scipy
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* scikit-learn
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* pandas
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* pygraphviz (for plotting causal graphs)
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* networkx (for analyzing causal graphs)
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* matplotlib (for general plotting)
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* sympy (for rendering symbolic expressions)
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On Ubuntu WSL/Windows 10, the following lines will install dependencies::
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pip3 install numpy
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pip3 install sklearn
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pip3 instlal pandas
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sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config
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## from https://github.com/pygraphviz/pygraphviz/issues/71
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pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" \
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--install-option="--library-path=/usr/lib/graphviz/"
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pip3 install networkx
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pip3 install matplotlib
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pip3 install sympy
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Questions/Feedback
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------------------
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For any general questions or feedback, you can reach us at the causal inference
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distribution list `causalinference@microsoft.com <mailto:causalinference@microsoft.com>`_.
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docs/source/docs/images

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../../images/

docs/source/dowhy.api.rst

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.. automodule:: dowhy.api.causal_data_frame
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:members:
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Module contents
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docs/source/dowhy.causal_estimators.rst

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dowhy.causal\_estimators.linear\_regression\_estimator module
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dowhy.causal\_estimators.propensity\_score\_matching\_estimator module
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dowhy.causal\_estimators.propensity\_score\_stratification\_estimator module
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dowhy.causal\_estimators.propensity\_score\_weighting\_estimator module
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dowhy.causal\_estimators.regression\_discontinuity\_estimator module
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docs/source/dowhy.causal_refuters.rst

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dowhy.causal\_refuters.placebo\_treatment\_refuter module
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dowhy.causal\_refuters.random\_common\_cause module
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docs/source/dowhy.data_transformers.dimensionality_reducers.rst

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docs/source/dowhy.data_transformers.rst

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dowhy.data_transformers.dimensionality_reducers
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dowhy.data_transformers.dimensionality_reducers
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docs/source/dowhy.do_samplers.rst

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