:parenttoc: True Release notes ============= .. tab-set:: .. tab-item:: Python .. dropdown:: DoubleML 0.7.0 :class-title: sd-bg-primary sd-font-weight-bold :open: - **Release highlight:** Benchmarking for Sensitivity Analysis (omitted variable bias) `#211 `_ - Policy tree estimation for the ``DoubleMLIRM`` class `#212 `_ - Extending sensitivity and policy tree documentation in User Guide and Example Gallery `#148 `_ `#150 `_ - The package requirements are set to python 3.8 or higher `#211 `_ - Maintenance documentation `#149 `_ - Maintenance package `#213 `_ .. dropdown:: DoubleML 0.6.3 :class-title: sd-bg-primary sd-font-weight-bold - Fix install requirements for 0.6.2 `#208 `_ .. dropdown:: DoubleML 0.6.2 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Sensitivity Analysis (omitted variable bias) for `#201 `_ - ``DoubleMLPLR`` - ``DoubleMLIRM`` - ``DoubleMLDID`` - ``DoubleMLDIDCS`` - Updated documentation `#144 `_ `#141 `_ - Extend the guide with sensitivity and add further examples `#142 `_ - Maintenance package `#202 `_ `#206 `_ - Maintenance documentation `#137 `_ `#138 `_ `#140 `_ `#143 `_ `#145 `_ `#146 `_ .. dropdown:: DoubleML 0.6.1 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Difference-in-differences models for ATTE estimation `#200 `_ `#194 `_ - Panel data ``DoubleMLDID`` - Repeated cross sections ``DoubleMLDIDCS`` - Add a potential time variable to ``DoubleMLData`` (until now only used in ``DoubleMLDIDCS``) `#200 `_ - Extend the guide in the documentation and add further examples `#132 `_ `#133 `_ `#135 `_ - Maintenance `#199 `_ `#134 `_ `#136 `_ .. dropdown:: DoubleML 0.6.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Heterogeneous treatment effects (GATE, CATE, Quantile effects, ...) - Add out-of-sample RMSE and targets for nuisance elements and implement nuisance estimation evaluation via ``evaluate_learners()``. `#182 `_ `#188 `_ - Implement ``gate()`` and ``cate()`` methods for ``DoubleMLIRM`` class. Both are based on the new ``DoubleMLBLP`` class. `#169 `_ - Implement different type of quantile models `#179 `_ - Potential quantiles (PQ) in class ``DoubleMLPQ`` - Local potential quantiles (LPQ) in class ``DoubleMLLPQ`` - Conditional value at risk (CVaR) in class ``DoubleMLCVAR`` - Quantile treatment effects (QTE) in class ``DoubleMLQTE`` - Extend clustering to nonlinear scores `#190 `_ - Add ``ipw_normalization`` option to ``DoubleMLIRM`` and ``DoubleMLIIVM`` `#186 `_ - Implement an abstract base class for data backends `#173 `_ - Extend the guide in the documentation and add further examples `#116 `_ `#125 `_ `#126 `_ - Code refactorings, bug fixes, docu updates, unit test extensions and continuous integration `#183 `_ `#192 `_ `#195 `_ `#196 `_ - Change License to BSD 3-Clause `#198 `_ - Maintenance `#174 `_ `#178 `_ `#181 `_ .. dropdown:: DoubleML 0.5.2 :class-title: sd-bg-primary sd-font-weight-bold - Fix / adapted unit tests which failed in the release of 0.5.1 to conda-forge `#172 `_ .. dropdown:: DoubleML 0.5.1 :class-title: sd-bg-primary sd-font-weight-bold - 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 `_ .. dropdown:: DoubleML 0.5.0 :class-title: sd-bg-primary sd-font-weight-bold - Implement a new score function ``score = 'IV-type'`` for the PLIV model (for details see `#151 `_) |br| --> **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 `_) |br| --> **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 `_ .. dropdown:: DoubleML 0.4.1 :class-title: sd-bg-primary sd-font-weight-bold - We added `Contribution Guidelines `_, issue templates, a pull request template and a `discussion forum `_ to the Python package repository `#132 `_ - Code refactorings, docu updates, unit test extensions and continuous integration `#126 `_ `#127 `_ `#128 `_ `#130 `_ `#131 `_ .. dropdown:: DoubleML 0.4.0 :class-title: sd-bg-primary sd-font-weight-bold - **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 `_ .. dropdown:: DoubleML 0.3.0 :class-title: sd-bg-primary sd-font-weight-bold - 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 `_ .. dropdown:: DoubleML 0.2.2 :class-title: sd-bg-primary sd-font-weight-bold - 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 `_. .. dropdown:: DoubleML 0.2.1 :class-title: sd-bg-primary sd-font-weight-bold - Provide an option to store & export the first-stage predictions `#91 `_ - Added the package logo to the doc .. dropdown:: DoubleML 0.2.0 :class-title: sd-bg-primary sd-font-weight-bold - 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 `_) .. dropdown:: DoubleML 0.1.2 :class-title: sd-bg-primary sd-font-weight-bold - 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. .. dropdown:: DoubleML 0.1.1 :class-title: sd-bg-primary sd-font-weight-bold - Bug fix in the drawing of bootstrap weights for the multiple treatment case `#66 `_ (see also https://github.com/DoubleML/doubleml-for-r/pull/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/ `_ .. dropdown:: DoubleML 0.1.0 :class-title: sd-bg-primary sd-font-weight-bold - 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. .. tab-item:: R .. dropdown:: DoubleML 0.5.3 :class-title: sd-bg-primary sd-font-weight-bold :open: - Add documentation for estimated models for nuisance parameters `#181 `_ - New contributor `@SvenKlaassen `_ - Maintenance `#179 `_ .. dropdown:: DoubleML 0.5.2 :class-title: sd-bg-primary sd-font-weight-bold - Store estimated models for nuisance parameters `#169 `_ - New maintainer of the CRAN package DoubleML `@PhilippBach `_ - Maintenance `#170 `_ `#173 `_ `#174 `_ `#177 `_ `#178 `_ .. dropdown:: DoubleML 0.5.1 :class-title: sd-bg-primary sd-font-weight-bold - Fix a CRAN issue (html checks) by regenerating ``.Rd``-files with the newest version of ``roxygen2``. `#166 `_ `#167 `_ `#168 `_ .. dropdown:: DoubleML 0.5.0 :class-title: sd-bg-primary sd-font-weight-bold - Implement a new score function ``score = 'IV-type'`` for the PLIV model (for details see `#161 `_) |br| --> **API change** from ``DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r [, ...])`` to ``DoubleMLPLIV$new(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 `#161 `_) |br| --> **API change** from ``DoubleMLPLR$new(obj_dml_data, ml_g, ml_m [, ...])`` to ``DoubleMLPLR$new(obj_dml_data, ml_l, ml_m, ml_g [, ...])`` - Use ``task_type`` instead of ``learner_class`` to identify whether a learner is meant to regress or classify (this change makes it possible to easily integrate pipelines from ``mlr3pipelines`` as learner for the nuisance functions) `#141 `_ - Add `Contribution Guidelines `_, issue templates, a pull request template and a `discussion forum `_ to the R package repository `#142 `_ `#146 `_ `#147 `_ - Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM `#114 `_ - Bug fixes and maintenance `#155 `_ `#156 `_ `#157 `_ `#158 `_ `#160 `_ `#163 `_ .. dropdown:: DoubleML 0.4.1 :class-title: sd-bg-primary sd-font-weight-bold - Prevent usage of ``glmnet`` learner for unit testing as recommended by CRAN (failing tests on Solaris) `#137 `_ - Prepare for the upcoming release of ``checkmate`` which is not backward compatible with our unit tests `#134 `_ .. dropdown:: DoubleML 0.4.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Clustered standard errors for double machine learning models `#119 `_ - Apply styler as described in the wiki (https://github.com/DoubleML/doubleml-for-r/wiki/Style-Guidelines) and add a corresponding CI on github actions `#120 `_ `#122 `_ - Other refactoring, bug fixes and documentation updates `#127 `_ `#129 `_ `#130 `_ `#131 `_ `#132 `_ `#133 `_ .. dropdown:: DoubleML 0.3.1 :class-title: sd-bg-primary sd-font-weight-bold - Initialize all numeric matrices, vectors and arrays with the correct data type by using ``NA_real_`` instead of ``NA`` and replace a ``print()`` call with ``cat()`` `#115 `_ .. dropdown:: DoubleML 0.3.0 :class-title: sd-bg-primary sd-font-weight-bold - Use active bindings in the R6 OOP implementation `#106 `_ & `#93 `_ - Fix the aggregation formula for standard errors from repeated cross-fitting `#94 `_ & `#95 `_ - Always use the same bootstrap algorithm independent of ``dml1`` vs ``dml2`` and consistent with docu and paper `#98 `_ & `#99 `_ - Initialize predictions with NA and make sure that there are no misleading entries in the evaluated score functions `#96 `_ & `#105 `_ - Avoid overriding learner parameters during tuning `#83 `_ & `#84 `_ - Fixes in the exception handling and extension of the unit tests for the score function choice `#82 `_ - Prevent overwriting parameters from initialization when calling set_ml_nuisance_params `#87 `_ & `#89 `_ - Major refactoring and cleanup and extension of the unit test framework `#101 `_ - Extension and reorganization of exception handling for ``DoubleMLData`` objects `#63 `_ & `#90 `_ - Introduce style guide and clean up code `#80 `_ & `#81 `_ - Adaption to be compatible with an API change in the next ``mlr3`` release `#103 `_ - Run unit tests with mlr3 in dev version on github actions `#104 `_ - Updated the citation info `#78 `_, `#79 `_ & `#86 `_ - Added a short version of and a reference to the arXiv paper as vignette `#110 `_ & `#113 `_ - Prevent using the subclassed methods check_score and check_data when constructing DoubleML objects `#107 `_ - Other refactoring and minor adaptions `#91 `_, `#92 `_, `#102 `_ & `#108 `_ .. dropdown:: DoubleML 0.2.1 :class-title: sd-bg-primary sd-font-weight-bold - Provide an option to store & export the first-stage predictions `#74 `_ - Reduce and refine messaging to the console during estimation `#72 `_ - Fix bug in IIVM model if the IV variable is not named ``z`` `#75 `_ - Fix failing unit test `#71 `_ - Added the package logo to the doc .. dropdown:: DoubleML 0.2.0 :class-title: sd-bg-primary sd-font-weight-bold - In the PLR one can now also specify classifiers for ``ml_m`` in case of a binary treatment variable with values 0 and 1 - Major refactoring of core-parts of the estimation and tuning of the ML estimators for the nuisance functions: All models now use central helper functions ``dml_cv_predict()`` and ``dml_tune()`` - Extensions to the unit test framework to improve upon test coverage - Added unit test coverage via codecov: `https://app.codecov.io/gh/DoubleML/doubleml-for-r `_ - Minor docu updates and adaptions: `#58 `_, `#61 `_ & `#70 `_ .. dropdown:: DoubleML 0.1.2 :class-title: sd-bg-primary sd-font-weight-bold - Adapt calls to ``mlr3tuning`` due to a change in their API (since version 0.6.0): fixes `#51 `_ - Add ``bbotk`` to suggests: fixes R CMD check note `#47 `_ - Use ``doi{}`` command: fixes R CMD check note `#54 `_ - Minor docu updates as ``DoubleML`` is now available on CRAN .. dropdown:: DoubleML 0.1.1 :class-title: sd-bg-primary sd-font-weight-bold - First release to CRAN `https://cran.r-project.org/package=DoubleML `_ - Clean up of imports - Continuous integration was extended by unit tests on github actions `https://github.com/DoubleML/doubleml-for-r/actions `_ .. dropdown:: DoubleML 0.1.0 :class-title: sd-bg-primary sd-font-weight-bold - Initial release - Development at `https://github.com/DoubleML/doubleml-for-r `_ - The R 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 ``DoubleML`` where the key elements of double machine learning are implemented. .. |br| raw:: html