Doubleml For Py Versions Save

DoubleML - Double Machine Learning in Python

0.4.1

2 years ago
  • 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

0.4.0

2 years ago
  • 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

0.3.0

3 years ago
  • Always use the same bootstrap algorithm independent of dml1 vs dml2 and 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 DoubleMLData objects #112
  • Code refactorings and unit test extensions #103, #105, #106, #111 & #113

0.2.2

3 years ago
  • 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 between x_cols and d_cols a column could have been added multiple times to the covariate matrix).
  • Added checks and exception handling for duplicate entries in d_cols, x_cols or z_cols (#100).
  • Check the datatype of data when initializing DoubleMLData objects. Also check for duplicate column names (#100).
  • Fix bug #95 in #97: It occurred when x_cols where inferred via setdiff and y_col was 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.

0.2.1

3 years ago
  • Provide an option to store & export the first-stage predictions #91
  • Added the package logo to the doc

0.2.0

3 years ago
  • 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_m in 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)

0.1.2

3 years ago
  • Fixed a compatibility issue with scikit-learn 0.24, 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.

0.1.1

3 years ago
  • 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

0.1.0

3 years ago
  • 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
  • All model classes are inherited from an abstract base class DoubleML where the key elements of double machine learning are implemented.

0.0.3

3 years ago