Recent Releases of doubleml-for-py
doubleml-for-py - DoubleML 0.10.1
Release highlight: Multi-Period Difference-in-Differences for Repeated Cross Sections
Allow user defined bandwidth for
RDFlexPy #343Maintenance documentation Docs #241 Docs #242 Docs #244 Docs #245 Docs #246
- Python
Published by SvenKlaassen 8 months ago
doubleml-for-py - DoubleML 0.10.0
Release highlight: Multi-Period Difference-in-Differences for Panel Data
Added Confidence sets which are robust to weak IVs:
robust_confset()method forDoubleMLIIVM(added by Ezequiel Smucler and David Masip) (Py #318, Docs #234)Update sensitivity operations to improve sensitivity bounds (Py #295)
Improve
DoubleMLAPOnuisance estimation and update weighted score elements. Added example to compareDoubleMLIRMandDoubleMLAPO(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 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)
- Python
Published by SvenKlaassen 9 months ago
doubleml-for-py - DoubleML 0.9.3
- Fixed/Adapted unit tests that failed in the release of 0.9.2 to conda-forge. #208
- Python
Published by SvenKlaassen about 1 year ago
doubleml-for-py - DoubleML 0.9.2
Make
rdrobustoptional for conda. Createpyproject.tomland removesetup.pyfor packaging #285 #286Maintenance package #284
- Python
Published by SvenKlaassen about 1 year ago
doubleml-for-py - DoubleML 0.9.1
Release highlight: Regression Discontinuity Designs with Flexible Covariate Adjustment via
RDFlexclass (in cooperation with Claudia Noack and Tomasz Olma; see their paper) #276Add
cov_type=HC0and enable key-worded arguments toDoubleMLBLP#270 #271Update User Guide and Example Gallery #204
Add AutoML example for tuning DoubleML estimators #199
- Python
Published by SvenKlaassen about 1 year ago
doubleml-for-py - DoubleML 0.9.0
Release highlight: Average potential outcomes for multiple discrete treatments via
DoubleMLAPOandDoubleMLAPOSclasses (proposed by Apoorva Lal) #245 #250Add sensitivity analysis to
DoubleMLFramework#249Maintenance documentation #182 #184 #186 #193 #194 #196 #197
- Python
Published by SvenKlaassen over 1 year ago
doubleml-for-py - DoubleML 0.8.2
API Update: Change nuisance evaluation for classifiers. The corresponding properties are renamed
nuisance_lossinstead ofrmses#254 #184Add new example on sensitivity analysis #190
Add a new example on DiD with DoubleML in R #178
Enable
set_sample_splittingfor cluster data #255Update the
make_confounded_irm_datadata generating process #263Maintainance package #264
- Python
Published by SvenKlaassen over 1 year ago
doubleml-for-py - 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
- Python
Published by SvenKlaassen over 1 year ago
doubleml-for-py - DoubleML 0.8.0
- Release highlight: Sample-selections models as
DoubleMLSMMclass (by Michaela Kecskésová) #231 #235 #171 - API change: Remove options
apply_crossfittinganddml_procedurefrom theDoubleMLclass #227 #166 - Restructure the package to improve readability and maintainability #225
- Add a
DoubleMLFrameworkclass 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_statisticstoutilsfor sensitivity analysis #229
- Python
Published by SvenKlaassen over 1 year ago
doubleml-for-py - DoubleML 0.7.1
Release highlight: Add weights to
DoubleMLIRMclass to extend sensitivity to GATEs etc. #220 #229 #155 #161Extend GATE and CATE estimation to the
DoubleMLPLRclass #220 #155Enable the use of external predictions for
DoubleMLclasses #221 #159Implementing utility classes and functions (gain statistics and dummy learners) #221 #222 #229 #161
- Python
Published by SvenKlaassen about 2 years ago
doubleml-for-py - DoubleML 0.7.0
Release highlight: Benchmarking for Sensitivity Analysis (omitted variable bias) #211
Policy tree estimation for the
DoubleMLIRMclass #212Extending 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
- Python
Published by SvenKlaassen over 2 years ago
doubleml-for-py - DoubleML 0.6.3
- Fix install requirements for 0.6.2 #208
- Python
Published by SvenKlaassen over 2 years ago
doubleml-for-py - DoubleML 0.6.2
Release highlight: Sensitivity Analysis (omitted variable bias) for #201
DoubleMLPLRDoubleMLIRMDoubleMLDIDDoubleMLDIDCS
Extend the guide with sensitivity and add further examples #142
- Python
Published by SvenKlaassen over 2 years ago
doubleml-for-py - DoubleML 0.6.1
DoubleML 0.6.1
Release highlight: Difference-in-differences models for ATTE estimation #200 #194 - Panel data
DoubleMLDID- Repeated cross sectionsDoubleMLDIDCSAdd a potential time variable to
DoubleMLData(until now only used inDoubleMLDIDCS) #200Extend the guide in the documentation and add further examples #132 #133 #135
- Python
Published by SvenKlaassen almost 3 years ago
doubleml-for-py - 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()andcate()methods forDoubleMLIRMclass. Both are based on the newDoubleMLBLPclass. #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
- Potential quantiles (PQ) in class
Extend clustering to nonlinear scores #190
Add
ipw_normalizationoption toDoubleMLIRMandDoubleMLIIVM#186Implement 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
- Python
Published by SvenKlaassen almost 3 years ago
doubleml-for-py - DoubleML 0.5.2
- Fix / adapted unit tests which failed in the release of 0.5.1 to conda-forge #172
- Python
Published by MalteKurz over 3 years ago
doubleml-for-py - DoubleML 0.5.1
- 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
- Python
Published by MalteKurz over 3 years ago
doubleml-for-py - DoubleML 0.5.0
- Implement a new score function
score = 'IV-type'for the PLIV model (for details see #151) --> API change fromDoubleMLPLIV(obj_dml_data, ml_g, ml_m, ml_r [, ...])toDoubleMLPLIV(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 fromDoubleMLPLR(obj_dml_data, ml_g, ml_m [, ...])toDoubleMLPLR(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
- Python
Published by MalteKurz over 3 years ago
doubleml-for-py - DoubleML 0.4.1
- We added Contribution Guidelines, issue templates, a pull request template and a discussion forum to the repository #132
- Code refactorings, docu updates, unit test extensions and continuous integration #126 #127 #128 #130 #131
- Python
Published by MalteKurz about 4 years ago
doubleml-for-py - DoubleML 0.4.0
- Release highlight: Clustered standard errors for double machine learning models #116
- Improve exception handling for missings and infinite values in the confounders, predictions, etc. (fixes #120 by allowing null confounder values) #122
- Clean up dev requirements and use dev requirements on github actions #121
- Other updates #123
- Python
Published by MalteKurz over 4 years ago
doubleml-for-py - DoubleML 0.3.0
- Always use the same bootstrap algorithm independent of
dml1vsdml2and consistent with docu and paper #101 & #102 - Added an exception handling to assure that an IV variable is specified when using a PLIV or IIVM model #107
- Improve exception handling for externally provided sample splitting #110
- Minor update of the str representation of
DoubleMLDataobjects #112 - Code refactorings and unit test extensions #103, #105, #106, #111 & #113
- Python
Published by MalteKurz over 4 years ago
doubleml-for-py - DoubleML 0.2.2
- IIVM model: Added a subgroups option to adapt to cases with and without the subgroups of always-takers and never-takers (#96).
- Add checks for the intersections of
y_col,d_cols,x_cols,z_cols(#84, #97). This also fixes #83 (with intersection betweenx_colsandd_colsa column could have been added multiple times to the covariate matrix). - Added checks and exception handling for duplicate entries in
d_cols,x_colsorz_cols(#100). - Check the datatype of
datawhen initializingDoubleMLDataobjects. Also check for duplicate column names (#100). - Fix bug #95 in #97: It occurred when
x_colswhere inferred via setdiff andy_colwas a string with multiple characters. - We updated the citation info to refer to the arXiv paper (#98): Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python, arXiv:2104.03220.
- Python
Published by MalteKurz almost 5 years ago
doubleml-for-py - DoubleML 0.2.1
- Provide an option to store & export the first-stage predictions #91
- Added the package logo to the doc
- Python
Published by MalteKurz almost 5 years ago
doubleml-for-py - DoubleML 0.2.0
- Major extensions of the unit test framework which result in a coverage >98% (a summary is given in #82)
- In the PLR one can now also specify classifiers for
ml_min case of a binary treatment variable with values 0 and 1 (see #86 for details) - The joint Python and R docu and user guide is now served to https://docs.doubleml.org from a separate repo https://github.com/DoubleML/doubleml-docs
- Generate and upload a unit test coverage report to codecov https://app.codecov.io/gh/DoubleML/doubleml-for-py #76
- Run lint checks with flake8 #78, align code with PEP8 standards #79, activate code quality checks at codacy #80
- Refactoring (reduce code redundancy) of the code for tuning of the ML learners used for approximation the nuisance functions #81
- Minor updates, bug fixes and improvements of the exception handling (contained in #82 & #89)
- Python
Published by MalteKurz almost 5 years ago
doubleml-for-py - DoubleML 0.1.2
- Fixed a compatibility issue with
scikit-learn0.24, which only affected some unit tests (#70, #71) - Added scheduled unit tests on github-action (three times a week) #69
- Split up estimation of nuisance functions and computation of score function components. Further introduced a private method
_est_causal_pars_and_se(), see #72. This is needed for the DoubleML-Serverless project: https://github.com/DoubleML/doubleml-serverless.
- Python
Published by MalteKurz about 5 years ago
doubleml-for-py - DoubleML 0.1.1
- Bug fix in the drawing of bootstrap weights for the multiple treatment case #66 (see also DoubleML/doubleml-for-r#28)
- Update install instructions as DoubleML is now listed on pypi
- Prepare submission to conda-forge: Include LICENSE file in source distribution
- Documentation is now served with HTTPS https://docs.doubleml.org
- Python
Published by MalteKurz about 5 years ago
doubleml-for-py - DoubleML 0.1.0
- Initial release
- Development at https://github.com/DoubleML/doubleml-for-py
- The Python package DoubleML provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018)).
- Implements double machine learning for four different models:
- Partially linear regression models (PLR) in class
DoubleMLPLR - Partially linear IV regression models (PLIV) in class
DoubleMLPLIV - Interactive regression models (IRM) in class
DoubleMLIRM - Interactive IV regression models (IIVM) in class
DoubleMLIIVM
- Partially linear regression models (PLR) in class
- All model classes are inherited from an abstract base class
DoubleMLwhere the key elements of double machine learning are implemented.
- Python
Published by MalteKurz about 5 years ago