spacebench
==========

.. image:: https://img.shields.io/badge/Dataverse-10.7910/DVN/SYNPBS-orange
   :target: https://www.doi.org/10.7910/DVN/SYNPBS

.. image:: https://img.shields.io/pypi/l/spacebench.svg
   :target: https://pypi.org/project/spacebench

.. image:: https://img.shields.io/pypi/v/spacebench.svg
   :target: https://pypi.org/project/spacebench


*SpaCE, the Spatial Confounding Environment, loads benchmark datasets for causal inference methods tackling spatial confounding*

The Spatial Confounding Environment loads benchmark datasets for causal inference that incorporartes spatial confounding. The SpaCE datasets contain real confounder and exposure/treatment data inspired by environmental health studies. The synthetic outcome and counterfactuals are generated for causal evaluation. They mimick real outcome data distributions learned with machine learning and neural network methods. Spatial confounding is achieved by masking influential confounders in the learned model.


.. toctree::
   :glob:
   :maxdepth: 2
   :caption: Introduction

   about

.. toctree::
   :glob:
   :maxdepth: 2
   :caption: Researchers

   setup_env
   quickstart
   modules
   

.. toctree::
   :glob:
   :maxdepth: 2
   :caption: Community
   
   contact
   CHANGELOG


Indices and tables
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* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`