DoubleML 0.5.2

• Fix / adapted unit tests which failed in the release of 0.5.1 to conda-forge #172

DoubleML 0.5.1

• Store estimated models for nuisance parameters #159
• Bug fix: Overwrite for tune method (introduced for depreciation warning) did not return the tune result #160 #162
• Maintenance #166 #167 #168 #170

DoubleML 0.5.0

• Implement a new score function score = 'IV-type' for the PLIV model (for details see #151)
  --> API change from DoubleMLPLIV(obj_dml_data, ml_g, ml_m, ml_r [, ...]) to DoubleMLPLIV(obj_dml_data, ml_g, ml_m, ml_r, ml_g [, ...])
• Adapt the nuisance estimation for the 'IV-type' score for the PLR model (for details see #151)
  --> API change from DoubleMLPLR(obj_dml_data, ml_g, ml_m [, ...]) to DoubleMLPLR(obj_dml_data, ml_l, ml_m, ml_g [, ...])
• Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM #134
• Published in JMLR: DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python (citation info updated in #138 )
• Maintenance #143 #148 #149 #152 #153

DoubleML 0.4.1

• We added Contribution Guidelines, issue templates, a pull request template and a discussion forum to the repository #132
• Code refactorings, docu updates, unit test extensions and continuous integration #126 #127 #128 #130 #131

DoubleML 0.4.0

• Release highlight: Clustered standard errors for double machine learning models #116
• Improve exception handling for missings and infinite values in the confounders, predictions, etc. (fixes #120 by allowing null confounder values) #122
• Clean up dev requirements and use dev requirements on github actions #121
• Other updates #123

DoubleML 0.3.0

• Always use the same bootstrap algorithm independent of dml1 vs dml2 and consistent with docu and paper #101 & #102
• Added an exception handling to assure that an IV variable is specified when using a PLIV or IIVM model #107
• Improve exception handling for externally provided sample splitting #110
• Minor update of the str representation of DoubleMLData objects #112
• Code refactorings and unit test extensions #103, #105, #106, #111 & #113

DoubleML 0.2.2

• IIVM model: Added a subgroups option to adapt to cases with and without the subgroups of always-takers and never-takers (#96).
• Add checks for the intersections of y_col, d_cols, x_cols, z_cols (#84, #97). This also fixes #83 (with intersection between x_cols and d_cols a column could have been added multiple times to the covariate matrix).
• Added checks and exception handling for duplicate entries in d_cols, x_cols or z_cols (#100).
• Check the datatype of data when initializing DoubleMLData objects. Also check for duplicate column names (#100).
• Fix bug #95 in #97: It occurred when x_cols where inferred via setdiff and y_col was a string with multiple characters.
• We updated the citation info to refer to the arXiv paper (#98): Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python, arXiv:2104.03220.

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.