Make example sections more prominent
Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>
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README.rst
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README.rst
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@ -125,13 +125,8 @@ first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL).
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pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \
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--install-option="--library-path=/usr/lib/graphviz/"
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Example usage
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~~~~~~~~~~~~~
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Two examples demonstrating the effect estimation and graphical causal models API.
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Effect identification and estimation
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++++++++++++++++++++++++++++++++++++
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Example: Effect identification and estimation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of
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a treatment on an outcome variable:
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@ -201,8 +196,8 @@ You can also use Conditional Average Treatment Effect (CATE) estimation methods
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"fit_params":{}})
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Graphical causal model (GCM) based inference
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++++++++++++++++++++++++++++++++++++++++++++
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Example: Graphical causal model (GCM) based inference
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation.
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It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable
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explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book
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