DoubleML 0.9.0

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

• Update User Guide and Example Gallery #188 #195

• Add sensitivity analysis to DoubleMLFramework #249

• Maintenance package #264 #265 #266

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

DoubleML 0.8.2

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

• Add new example on sensitivity analysis #190

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

• Enable set_sample_splitting for cluster data #255

• Update the make_confounded_irm_data data generating process #263

• Maintainance package #264

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

DoubleML 0.8.1

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

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

• Maintenance documentation #172

DoubleML 0.8.0

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

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

• Restructure the package to improve readability and maintainability #225

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

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

• Add the gain_statistics to utils for sensitivity analysis #229

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

• Maintenance package #225 #229 #246

DoubleML 0.7.1

• 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

DoubleML 0.7.0

• 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

DoubleML 0.6.3

• Fix install requirements for 0.6.2 #208

DoubleML 0.6.2

• 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

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

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