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Releases: DoubleML/doubleml-for-py

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

DoubleML 0.6.2

21 Jun 14:28
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  • 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

08 May 07:39
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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

04 Apr 13:16
841dbf6
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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

DoubleML 0.5.2

14 Nov 10:43
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  • Fix / adapted unit tests which failed in the release of 0.5.1 to conda-forge #172

DoubleML 0.5.1

11 Nov 14:30
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  • 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

DoubleML 0.5.0

14 Jun 09:17
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  • 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

DoubleML 0.4.1

20 Dec 09:34
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