Doubleml For Py Versions Save

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

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

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

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

• Extend example Gallery #153 #158 #161

• Maintenance documentation #157 #160

• Maintenance package #223 #224

• 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

• Fix install requirements for 0.6.2 #208

• 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

DoubleML 0.6.1

• 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

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(). #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

• 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

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

• 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

• 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

• 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