DoubleML 0.2.1

• Provide an option to store & export the first-stage predictions #91
• Added the package logo to the doc

DoubleML 0.2.0

• Major extensions of the unit test framework which result in a coverage >98% (a summary is given in #82)
• In the PLR one can now also specify classifiers for ml_m in case of a binary treatment variable with values 0 and 1 (see #86 for details)
• The joint Python and R docu and user guide is now served to https://docs.doubleml.org from a separate repo https://github.com/DoubleML/doubleml-docs
• Generate and upload a unit test coverage report to codecov https://app.codecov.io/gh/DoubleML/doubleml-for-py #76
• Run lint checks with flake8 #78, align code with PEP8 standards #79, activate code quality checks at codacy #80
• Refactoring (reduce code redundancy) of the code for tuning of the ML learners used for approximation the nuisance functions #81
• Minor updates, bug fixes and improvements of the exception handling (contained in #82 & #89)

DoubleML 0.1.2

• Fixed a compatibility issue with scikit-learn 0.24, which only affected some unit tests (#70, #71)
• Added scheduled unit tests on github-action (three times a week) #69
• Split up estimation of nuisance functions and computation of score function components. Further introduced a private method _est_causal_pars_and_se(), see #72. This is needed for the DoubleML-Serverless project: https://github.com/DoubleML/doubleml-serverless.

DoubleML 0.1.1

• Bug fix in the drawing of bootstrap weights for the multiple treatment case #66 (see also DoubleML/doubleml-for-r#28)
• Update install instructions as DoubleML is now listed on pypi
• Prepare submission to conda-forge: Include LICENSE file in source distribution
• Documentation is now served with HTTPS https://docs.doubleml.org

DoubleML 0.1.0

• Initial release
• Development at https://github.com/DoubleML/doubleml-for-py
• The Python package DoubleML provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018)).
• Implements double machine learning for four different models:
  • Partially linear regression models (PLR) in class DoubleMLPLR
  • Partially linear IV regression models (PLIV) in class DoubleMLPLIV
  • Interactive regression models (IRM) in class DoubleMLIRM
  • Interactive IV regression models (IIVM) in class DoubleMLIIVM
• All model classes are inherited from an abstract base class DoubleML where the key elements of double machine learning are implemented.

DoubleML 0.0.3

create another test pre-release to test the github actions integrations

DoubleML 0.0.2

Merge branch 'master' of github.com:DoubleML/doubleml-for-py into 0.0.X

DoubleML 0.0.1

Merge branch 'master' of github.com:DoubleML/doubleml-for-py into 0.0.X