Doubleml

- **Release highlight:** Difference-in-differences models for ATTE estimation [200](https://github.com/DoubleML/doubleml-for-py/pull/200) [#194](https://github.com/DoubleML/doubleml-for-py/issues/194)
- Panel data `DoubleMLDID`
- Repeated cross sections `DoubleMLDIDCS`

- Add a potential time variable to `DoubleMLData` (until now only used in `DoubleMLDIDCS`) [200](https://github.com/DoubleML/doubleml-for-py/pull/200)

- Extend the guide in the documentation and add further examples [132](https://github.com/DoubleML/doubleml-docs/pull/132) [#133](https://github.com/DoubleML/doubleml-docs/pull/133) [#135](https://github.com/DoubleML/doubleml-docs/pull/135)

- Maintenance [199](https://github.com/DoubleML/doubleml-for-py/pull/199) [#134](https://github.com/DoubleML/doubleml-docs/pull/134) [#136](https://github.com/DoubleML/doubleml-docs/pull/136)

- **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](https://github.com/DoubleML/doubleml-for-py/pull/182) [#188](https://github.com/DoubleML/doubleml-for-py/pull/188)
- Implement `gate()` and `cate()` methods for `DoubleMLIRM` class. Both are
based on the new `DoubleMLBLP` class. [169](https://github.com/DoubleML/doubleml-for-py/pull/169)
- Implement different type of quantile models [179](https://github.com/DoubleML/doubleml-for-py/pull/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](https://github.com/DoubleML/doubleml-for-py/pull/190)
- Add `ipw_normalization` option to `DoubleMLIRM` and `DoubleMLIIVM` [186](https://github.com/DoubleML/doubleml-for-py/pull/186)
- Implement an abstract base class for data backends [173](https://github.com/DoubleML/doubleml-for-py/pull/173)
- Code refactorings, bug fixes, docu updates, unit test extensions and continuous integration [183](https://github.com/DoubleML/doubleml-for-py/pull/183) [#192](https://github.com/DoubleML/doubleml-for-py/pull/192) [#195](https://github.com/DoubleML/doubleml-for-py/pull/195) [#196](https://github.com/DoubleML/doubleml-for-py/pull/196)
- Change License to BSD 3-Clause [198](https://github.com/DoubleML/doubleml-for-py/pull/198)
- Maintenance [174](https://github.com/DoubleML/doubleml-for-py/pull/174) [#178](https://github.com/DoubleML/doubleml-for-py/pull/178) [#181](https://github.com/DoubleML/doubleml-for-py/pull/181)

- Add documentation for estimated models for nuisance parameters [181](https://github.com/DoubleML/doubleml-for-r/pull/181)
- New contributor SvenKlaassen
- Maintenance [179](https://github.com/DoubleML/doubleml-for-r/pull/179)

- 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