DoubleML 0.10.0

• Release highlight: Multi-Period Difference-in-Differences for Panel Data

  • Implementation via DoubleMLDIDMulti class (Py #292, Py #315)
  • New doubleml.data submodule including DoubleMLData and DoubleMLPanelData classes (Py #292)
  • Extended User Guide and Example Gallery (Docs #224, Docs #233, Docs #237)

• Added Confidence sets which are robust to weak IVs: robust_confset() method for DoubleMLIIVM (added by Ezequiel Smucler and David Masip) (Py #318, Docs #234)

• Update sensitivity operations to improve sensitivity bounds (Py #295)

• Improve DoubleMLAPO nuisance estimation and update weighted score elements. Added example to compare DoubleMLIRM and DoubleMLAPO (Py #295, Py #297, Docs #220)

• Updated variance aggregation over repetitions via confidence intervals (Py #324, Docs #236)

• Added a separate package citation using CITATION.cff (Py #321)

• Update package formatting, linting and add pre-commit hooks (Py #288, Py #289, Py #294, Py #316)

• Maintenance package (Py #287, Py #288, Py #291, Py #319)

• Maintenance documentation (Docs #211, Docs #213, Docs #214, Docs #215, Docs #216, Docs #217, Docs #218, Docs #219, Docs #221, Docs #225, Docs #227, Docs #228, Docs #229, Docs #230, Docs #232, Docs #238, Docs #239)

DoubleML 0.9.3

• Fixed/Adapted unit tests that failed in the release of 0.9.2 to conda-forge. #208

DoubleML 0.9.2

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

• Maintenance package #284

• Maintenance documentation #205 #206 #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) #276

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

• Update User Guide and Example Gallery #204

• Add AutoML example for tuning DoubleML estimators #199

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

• Maintenance documentation #201 #203

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