Merge pull request #67 from pymc-labs/doc-improvements

Doc improvements

Author: drbenvincent

.gitignore

@@ -7,3 +7,4 @@ _build
 build/
 dist/
 docs/_build/
+*.vscode

README.md

@@ -1,6 +1,7 @@
 # CausalPy
 
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![PyPI version](https://badge.fury.io/py/CausalPy.svg)](https://badge.fury.io/py/CausalPy)
 
 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.
 

docs/examples.rst

@@ -35,6 +35,6 @@ Regression Discontinuity
    :titlesonly:
 
    notebooks/rd_pymc.ipynb
-   notebooks/rd_pymc_drinking.ipynb
    notebooks/rd_skl.ipynb
+   notebooks/rd_pymc_drinking.ipynb
    notebooks/rd_skl_drinking.ipynb

docs/index.rst

@@ -3,10 +3,54 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-CausalPy - Causal inference in quasi-experimental settings
-==========================================================
+CausalPy - causal inference for quasi-experiments
+=================================================
 
-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.
+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.
+
+Installation
+------------
+
+To get the latest release:
+
+.. code-block:: sh
+
+   pip install CausalPy
+
+Alternatively, if you want the very latest version of the package you can install from GitHub:
+
+.. code-block:: sh
+
+   pip install git+https://github.com/pymc-labs/CausalPy.git
+
+
+Features
+--------
+
+Different quasi-experimental methods
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Rather than focussing on one particular quasi-experimental setting, this package aims to have broad applicability. We can analyse data from the following quasi-experimental methods:
+
+Synthetic control
+"""""""""""""""""
+
+.. image:: ../img/synthetic_control_pymc.svg
+
+Interrupted time series
+"""""""""""""""""""""""
+
+.. image:: ../img/interrupted_time_series_pymc.svg
+
+Difference in differences
+"""""""""""""""""""""""""
+
+.. image:: ../img/difference_in_differences_pymc.svg
+
+Regression discontinuity
+""""""""""""""""""""""""
+
+.. image:: ../img/regression_discontinuity_pymc.svg
 
 Support
 -------