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Releases: DoubleML/doubleml-for-py

DoubleML 0.9.3

09 Jan 14:51
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  • Fixed/Adapted unit tests that failed in the release of 0.9.2 to conda-forge. #208

DoubleML 0.9.2

08 Jan 11:22
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  • 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

06 Jan 12:15
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  • 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

30 Aug 14:52
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DoubleML 0.8.2

05 Aug 15:56
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  • 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

10 Jun 13:15
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  • 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

06 Jun 14:19
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  • 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

02 Feb 12:57
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  • 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

18 Sep 12:38
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  • 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

26 Jun 12:40
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  • Fix install requirements for 0.6.2 #208