.. _guide:

:parenttoc: True

User Guide
==========

.. toctree::
   :maxdepth: 2
   :numbered:

   The basics of double/debiased machine learning <basics>
   Data Backend <data_backend>
   Models <models>
   Heterogeneous Treatment Effects <heterogeneity>
   Score functions <scores>
   Double machine learning algorithms <algorithms>
   Learners, hyperparameters and hyperparameter tuning <learners>
   Variance estimation and confidence intervals <se_confint>
   Sample-splitting, cross-fitting and repeated cross-fitting <resampling>
   Sensitivity Analysis <sensitivity>