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You can adapt this file completely to your liking, but it should at least
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CausalPy - Causal inference in quasi-experimental settings
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==========================================================
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CausalPy - causal inference for quasi-experiments
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=================================================
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A Python package focussing on causal inference in quasi-experimental settings. The package allows for sophisticated Bayesian model fitting methods to be used in addition to traditional OLS.
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A Python package focussing on causal inference for quasi-experimentas. The package allows users to use different model types. Sophisticated Bayesian methods can be used, harnessing the power of `PyMC <https://www.pymc.io/>`_ and `ArviZ <https://python.arviz.org>`_. But users can also use more traditional `Ordinary Least Squares <https://en.wikipedia.org/wiki/Ordinary_least_squares>`_ estimation methods via `scikit-learn <https://scikit-learn.org/>`_ models.
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Installation
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------------
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To get the latest release:
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.. code-block:: sh
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pip install CausalPy
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Alternatively, if you want the very latest version of the package you can install from GitHub:
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.. code-block:: sh
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pip install git+https://github.com/pymc-labs/CausalPy.git
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Features
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--------
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Rather than focussing on one particular quasi-experimental setting, this package aims to have broad applicability.
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Synthetic control
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^^^^^^^^^^^^^^^^^
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Interrupted time series
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^^^^^^^^^^^^^^^^^^^^^^^
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Difference in differences
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^^^^^^^^^^^^^^^^^^^^^^^^^
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Regression discontinuity
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^^^^^^^^^^^^^^^^^^^^^^^^
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Support
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-------

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