Release Notes

Python

DoubleML 0.9.3

• Fix / adapted unit tests which failed in the release of 0.9.2 to conda-forge Docs #208

DoubleML 0.9.2

• Make rdrobust optional for conda. Create pyproject.toml and remove setup.py for packaging Py #285 Py #286

• Maintenance package Py #284

• Maintenance doccumentation Docs #205 Docs #206 Docs #207

DoubleML 0.9.1

• Release highlight: Regression Discontinuity Designs with Flexible Covariate Adjustment via RDFlex class (in cooperation with Claudia Noack and Tomasz Olma; see their paper) Py #276

• Add cov_type=HC0 and enable key-worded arguments to DoubleMLBLP Py #270 Py #271

• Update User Guide and Example Gallery Docs #204

• Add AutoML example for tuning DoubleML estimators Docs #199

• Maintenance package Py #268 Py #278 Py #279 Py #281 Py #282

• Maintenance documentation Docs #201 Docs #203

DoubleML 0.9.0

• Release highlight: Average potential outcomes for multiple discrete treatments via DoubleMLAPO and DoubleMLAPOS classes (proposed by Apoorva Lal) Py #245 Py #250

• Update User Guide and Example Gallery Docs #188 Docs #195

• Add sensitivity analysis to DoubleMLFramework Py #249

• Maintenance package Py #264 Py #265 Py #266

• Maintenance documentation Docs #182 Docs #184 Docs #186 Docs #193 Docs #194 Docs #196 Docs #197

DoubleML 0.8.2

• API Update: Change nuisance evaluation for classifiers. The corresponding properties are renamed nuisance_loss instead of rmses. Py #254 Docs #184

• Add new example on sensitivity analysis Docs #190

• Add a new example on DiD with DoubleML in R Docs #178

• Enable set_sample_splitting for cluster data Py #255

• Update the make_confounded_irm_data data generating process Py #263

• Maintenance package Py #264

• Maintenance documentation Docs #177 Docs #180 Docs #181 Docs #187 Docs #189

DoubleML 0.8.1

• Increment package requirements and update workflows for python 3.9 (add tests for python 3.12) Py #247 Docs #175

• Additional example for ranking treatment effects (by Apoorva Lal) Docs #173 Docs #174

• Maintenance documentation Docs #172

DoubleML 0.8.0

• Release highlight: Sample-selections models as DoubleMLSMM class (by Michaela Kecskésová) Py #231 Py #235 Docs #171

• API change: Remove options apply_crossfitting and dml_procedure from the DoubleML class Py #227 Docs #166

• Restructure the package to improve readability and maintainability Py #225

• Add a DoubleMLFramework class to combine multiple DoubleML models (aggregation of estimates, boostrap and CI-procedures) Py #226 Docs #169

• Enable the use of external predictions for short models in benchmarks (by Lucien) Py #238 Py #239

• Add the gain_statistics to utils to sensitivity analysis Py #229

• Maintenance documentation Docs #162 Docs #163 Docs #164 Docs #165 Docs #167 Docs #168

• Maintenance package Py #225 Py #229 Py #246

DoubleML 0.7.1

• Release highlight: Add weights to DoubleMLIRM class to extend sensitivity to GATEs etc. Py #220 Py #229 Docs #155 Docs #161

• Extend GATE and CATE estimation to the DoubleMLPLR class Py #220 Docs #155

• Enable the use of external predictions for DoubleML classes Py #221 Docs #159

• Implementing utility classes and functions (gain statistics and dummy learners) Py #221 Py #222 Py #229 Docs #161

• Extend example Gallery Docs #153 Docs #158 Docs #161

• Maintenance documentation Docs #157 Docs #160

• Maintenance package Py #223 Py #224

DoubleML 0.7.0

• Release highlight: Benchmarking for Sensitivity Analysis (omitted variable bias) Py #211

• Policy tree estimation for the DoubleMLIRM class Py #212

• Extending sensitivity and policy tree documentation in User Guide and Example Gallery Docs #148 Docs #150

• The package requirements are set to python 3.8 or higher Py #211

• Maintenance documentation Docs #149

• Maintenance package Py #213

DoubleML 0.6.3

• Fix install requirements for 0.6.2 Py #208

DoubleML 0.6.2

• Release highlight: Sensitivity Analysis (omitted variable bias) for Py #201

  • DoubleMLPLR

  • DoubleMLIRM

  • DoubleMLDID

  • DoubleMLDIDCS

• Updated documentation Docs #144 Docs #141

• Extend the guide with sensitivity and add further examples Docs #142

• Maintenance package Py #202 Py #206

• Maintenance documentation Docs #137 Docs #138 Docs #140 Docs #143 Docs #145 Docs #146

DoubleML 0.6.1

• Release highlight: Difference-in-differences models for ATTE estimation Py #200 Py #194

  • Panel data DoubleMLDID

  • Repeated cross sections DoubleMLDIDCS

• Add a potential time variable to DoubleMLData (until now only used in DoubleMLDIDCS) Py #200

• Extend the guide in the documentation and add further examples Docs #132 Docs #133 Docs #135

• Maintenance Py #199 Docs #134 Docs #136

DoubleML 0.6.0

• 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(). Py #182 Py #188

• Implement gate() and cate() methods for DoubleMLIRM class. Both are based on the new DoubleMLBLP class. Py #169

• Implement different type of quantile models Py #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 Py #190

• Add ipw_normalization option to DoubleMLIRM and DoubleMLIIVM Py #186

• Implement an abstract base class for data backends Py #173

• Extend the guide in the documentation and add further examples Docs #116 Docs #125 Docs #126

• Code refactorings, bug fixes, docu updates, unit test extensions and continuous integration Py #183 Py #192 Py #195 Py #196

• Change License to BSD 3-Clause Py #198

• Maintenance Py #174 Py #178 Py #181

DoubleML 0.5.2

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

DoubleML 0.5.1

• Store estimated models for nuisance parameters Py #159

• Bug fix: Overwrite for tune method (introduced for depreciation warning) did not return the tune result Py #160 Py #162

• Maintenance Py #166 Py #167 Py #168 Py #170

DoubleML 0.5.0

• Implement a new score function score = 'IV-type' for the PLIV model (for details see Py #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 Py #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 Py #134

• Published in JMLR: DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python (citation info updated in Py #138)

• Maintenance Py #143 Py #148 Py #149 Py #152 Py #153

DoubleML 0.4.1

• We added Python Contribution Guidelines, issue templates, a pull request template and a Python discussion forum to the Python package repository Py #132

• Code refactorings, docu updates, unit test extensions and continuous integration Py #126 Py #127 Py #128 Py #130 Py #131

DoubleML 0.4.0

• Release highlight: Clustered standard errors for double machine learning models Py #116

• Improve exception handling for missings and infinite values in the confounders, predictions, etc. (fixes Py #120 by allowing null confounder values) Py #122

• Clean up dev requirements and use dev requirements on github actions Py #121

• Other updates Py #123

DoubleML 0.3.0

• Always use the same bootstrap algorithm independent of dml1 vs dml2 and consistent with docu and paper Py #101 & Py #102

• Added an exception handling to assure that an IV variable is specified when using a PLIV or IIVM model Py #107

• Improve exception handling for externally provided sample splitting Py #110

• Minor update of the str representation of DoubleMLData objects Py #112

• Code refactorings and unit test extensions Py #103, Py #105, Py #106, Py #111 & Py #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 (Py #96).

• Add checks for the intersections of y_col, d_cols, x_cols, z_cols (Py #84, Py #97). This also fixes Py #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 (Py #100).

• Check the datatype of data when initializing DoubleMLData objects. Also check for duplicate column names (Py #100).

• Fix bug Py #95 in Py #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 (Py #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 Py #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 Py #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 Py #86 for details)

• The joint Python and R docu and user guide is now served to https://docs.doubleml.org from a separate repo DoubleML/doubleml-docs

• Generate and upload a unit test coverage report to codecov https://app.codecov.io/gh/DoubleML/doubleml-for-py Py #76

• Run lint checks with flake8 Py #78, align code with PEP8 standards Py #79, activate code quality checks at codacy Py #80

• Refactoring (reduce code redundancy) of the code for tuning of the ML learners used for approximation the nuisance functions Py #81

• Minor updates, bug fixes and improvements of the exception handling (contained in Py #82 & Py #89)

DoubleML 0.1.2

• Fixed a compatibility issue with scikit-learn 0.24, which only affected some unit tests (Py #70, Py #71)

• Added scheduled unit tests on github-action (three times a week) Py #69

• Split up estimation of nuisance functions and computation of score function components. Further introduced a private method _est_causal_pars_and_se(), see Py #72. This is needed for the DoubleML-Serverless project: DoubleML/doubleml-serverless.

DoubleML 0.1.1

• Bug fix in the drawing of bootstrap weights for the multiple treatment case Py #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 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.

R

DoubleML 1.0.1

• Maintenance (upcoming breaking changes from paradox package), thanks to new contributor Martin Binder @mb706 R #195 R #198

DoubleML 1.0.0

• Update citation info to publication in Journal of Statistical Software, rename helper function and fix links and GH actions R #191

DoubleML 0.5.3

• Add documentation for estimated models for nuisance parameters R #181

• New contributor @SvenKlaassen

• Maintenance R #179

DoubleML 0.5.2

• Store estimated models for nuisance parameters R #169

• New maintainer of the CRAN package DoubleML @PhilippBach

• Maintenance R #170 R #173 R #174 R #177 R #178

DoubleML 0.5.1

• Fix a CRAN issue (html checks) by regenerating .Rd-files with the newest version of roxygen2. R #166 R #167 R #168

DoubleML 0.5.0

• Implement a new score function score = 'IV-type' for the PLIV model (for details see R #161)
  –> 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 R #161)
  –> 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) R #141

• Add R Contribution Guidelines, issue templates, a pull request template and a R discussion forum to the R package repository R #142 R #146 R #147

• Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM R #114

• Bug fixes and maintenance R #155 R #156 R #157 R #158 R #160 R #163

DoubleML 0.4.1

• Prevent usage of glmnet learner for unit testing as recommended by CRAN (failing tests on Solaris) R #137

• Prepare for the upcoming release of checkmate which is not backward compatible with our unit tests R #134

DoubleML 0.4.0

• Release highlight: Clustered standard errors for double machine learning models R #119

• Apply styler as described in the wiki (DoubleML/doubleml-for-r) and add a corresponding CI on github actions R #120 R #122

• Other refactoring, bug fixes and documentation updates R #127 R #129 R #130 R #131 R #132 R #133

DoubleML 0.3.1

• 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() R #115

DoubleML 0.3.0

• Use active bindings in the R6 OOP implementation R #106 & R #93

• Fix the aggregation formula for standard errors from repeated cross-fitting R #94 & R #95

• Always use the same bootstrap algorithm independent of dml1 vs dml2 and consistent with docu and paper R #98 & R #99

• Initialize predictions with NA and make sure that there are no misleading entries in the evaluated score functions R #96 & R #105

• Avoid overriding learner parameters during tuning R #83 & R #84

• Fixes in the exception handling and extension of the unit tests for the score function choice R #82

• Prevent overwriting parameters from initialization when calling set_ml_nuisance_params R #87 & R #89

• Major refactoring and cleanup and extension of the unit test framework R #101

• Extension and reorganization of exception handling for DoubleMLData objects R #63 & R #90

• Introduce style guide and clean up code R #80 & R #81

• Adaption to be compatible with an API change in the next mlr3 release R #103

• Run unit tests with mlr3 in dev version on github actions R #104

• Updated the citation info R #78, R #79 & R #86

• Added a short version of and a reference to the arXiv paper as vignette R #110 & R #113

• Prevent using the subclassed methods check_score and check_data when constructing DoubleML objects R #107

• Other refactoring and minor adaptions R #91, R #92, R #102 & R #108

DoubleML 0.2.1

• Provide an option to store & export the first-stage predictions R #74

• Reduce and refine messaging to the console during estimation R #72

• Fix bug in IIVM model if the IV variable is not named z R #75

• Fix failing unit test R #71

• Added the package logo to the doc

DoubleML 0.2.0

• 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: R #58, R #61 & R #70

DoubleML 0.1.2

• Adapt calls to mlr3tuning due to a change in their API (since version 0.6.0): fixes R #51

• Add bbotk to suggests: fixes R CMD check note R #47

• Use doi{} command: fixes R CMD check note R #54

• Minor docu updates as DoubleML is now available on CRAN

DoubleML 0.1.1

• 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 DoubleML/doubleml-for-r

DoubleML 0.1.0

• Initial release

• Development at 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.