Doubleml

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

- Fixed a compatibility issue with `scikit-learn` 0.24, which only affected some unit tests ([70](https://github.com/DoubleML/doubleml-for-py/issues/70), [#71](https://github.com/DoubleML/doubleml-for-py/pull/71))
- Added scheduled unit tests on github-action (three times a week) [69](https://github.com/DoubleML/doubleml-for-py/pull/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](https://github.com/DoubleML/doubleml-for-py/pull/72). This is needed for the DoubleML-Serverless project: https://github.com/DoubleML/doubleml-serverless.

- Bug fix in the drawing of bootstrap weights for the multiple treatment case 66 (see also DoubleML/doubleml-for-r28)
- 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

- Initial release
- Development at [https://github.com/DoubleML/doubleml-for-py](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)](https://doi.org/10.1111/ectj.12097)).
- 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.