Skip to content

CHANGELOG.md

Latest commit

 

History

History
141 lines (90 loc) · 7.33 KB

CHANGELOG.md

File metadata and controls

141 lines (90 loc) · 7.33 KB
Raw

Changelog

0.14.0 (2025-07-xx)

  • Remove polars as a run dependency.
  • Make [MetaLearner.feature_importances][metalearners.metalearner.MetaLearner.feature_importances] and [Explainer.feature_importances][metalearners.explainer.Explainer.feature_importances] return a list of dicts, rather than a list of pandas.DataFrames.

0.13.0 (2025-05-19)

New features

  • Add support for polars input in all fit* and predict* functions.

0.12.0 (2025-01-29)

Other changes

  • Comply with scikit-learn versions 1.6 and higher.

0.11.0 (2024-09-05)

New features

  • Add support for using scipy.sparse.csr_matrix as datastructure for covariates X.

0.10.0 (2024-08-13)

New features

  • Add abstract method [MetaLearner.predict_conditional_average_outcomes][metalearners.metalearner.MetaLearner.predict_conditional_average_outcomes] to [metalearners.metalearner.MetaLearner][metalearners.metalearner.MetaLearner].
  • Implement [RLearner.predict_conditional_average_outcomes][metalearners.rlearner.RLearner.predict_conditional_average_outcomes] for [metalearners.rlearner.RLearner][metalearners.rlearner.RLearner].

Bug fixes

  • Fix bug in which the [metalearners.slearner.SLearner][metalearners.slearner.SLearner]'s inference step would have some leakage in the in-sample scenario.

0.9.0 (2024-08-02)

New features

  • Add [MetaLearner.init_args][metalearners.metalearner.MetaLearner.init_args].
  • Add [FixedBinaryPropensity][metalearners.utils.FixedBinaryPropensity].
  • Add MetaLearner._build_onnx to [metalearners.MetaLearner][metalearners.metalearner.MetaLearner] abstract class and implement it for [TLearner][metalearners.tlearner.TLearner], [XLearner][metalearners.xlearner.XLearner], [RLearner][metalearners.rlearner.RLearner], and [DRLearner][metalearners.drlearner.DRLearner].
  • Add MetaLearner._necessary_onnx_models.
  • Add [DRLearner.average_treatment_effect][metalearners.drlearner.DRLearner.average_treatment_effect] to compute the AIPW point estimate and standard error for average treatment effects (ATE) without requiring a full model fit.

0.8.0 (2024-07-22)

New features

  • Add [MetaLearner.fit_all_nuisance][metalearners.metalearner.MetaLearner.fit_all_nuisance] and [MetaLearner.fit_all_treatment][metalearners.metalearner.MetaLearner.fit_all_treatment].
  • Add optional store_raw_results and store_results parameters to [MetaLearnerGridSearch][metalearners.grid_search.MetaLearnerGridSearch].
  • Renamed _GSResult to [GSResult][metalearners.grid_search.GSResult].
  • Added grid_size_ attribute to [MetaLearnerGridSearch][metalearners.grid_search.MetaLearnerGridSearch].
  • Implement [CrossFitEstimator.score][metalearners.cross_fit_estimator.CrossFitEstimator.score].

Bug fixes

  • Fixed a bug in [MetaLearner.evaluate][metalearners.metalearner.MetaLearner.evaluate] where it failed in the case of feature_set being different from None.

0.7.0 (2024-07-12)

New features

  • Add optional adaptive_clipping parameter to [DRLearner][metalearners.drlearner.DRLearner].

Other changes

  • Change the index columns order in MetaLearnerGridSearch.results_.
  • Raise a custom error if only one class is present in a classification outcome.
  • Raise a custom error if there are some treatment variants which have seen classification outcomes that have not appeared for some other treatment variant.

0.6.0 (2024-07-08)

New features

  • Implement [MetaLearnerGridSearch][metalearners.grid_search.MetaLearnerGridSearch].
  • Add a scoring parameter to [MetaLearner.evaluate][metalearners.metalearner.MetaLearner.evaluate] and implement the abstract method for [XLearner][metalearners.xlearner.XLearner] and [DRLearner][metalearners.drlearner.DRLearner].

Other changes

  • Increase the lower bound on scikit-learn from 1.3 to 1.4.
  • Drop the run dependency on git_root.

0.5.0 (2024-06-18)

  • No longer raise an error if feature_set is provided to [SLearner][metalearners.slearner.SLearner].
  • Fix a bug where base model dictionaries -- e.g., n_folds or feature-set -- were improperly initialized if the provided dictionary's keys were a strict superset of the expected keys.

0.4.2 (2024-06-18)

  • Ship license file.

0.4.1 (2024-06-18)

  • Fix dependencies for pip.

0.4.0 (2024-06-18)

  • Implemented [CrossFitEstimator.clone][metalearners.cross_fit_estimator.CrossFitEstimator.clone].
  • Added n_jobs_base_learners to [MetaLearner.fit][metalearners.metalearner.MetaLearner.fit].
  • Renamed [Explainer.feature_importances][metalearners.explainer.Explainer.feature_importances]. Note this is a breaking change.
  • Renamed [MetaLearner.feature_importances][metalearners.metalearner.MetaLearner.feature_importances]. Note this is a breaking change.
  • Renamed [Explainer.shap_values][metalearners.explainer.Explainer.shap_values]. Note this is a breaking change.
  • Renamed [MetaLearner.shap_values][metalearners.metalearner.MetaLearner.shap_values]. Note this is a breaking change.
  • Renamed [MetaLearner.explainer][metalearners.metalearner.MetaLearner.explainer]. Note this is a breaking change.
  • Implemented synchronize_cross_fitting parameter for [MetaLearner.fit][metalearners.metalearner.MetaLearner.fit].
  • Implemented cv parameter for [CrossFitEstimator.fit][metalearners.cross_fit_estimator.CrossFitEstimator.fit].

0.3.0 (2024-06-03)

  • Implemented [Explainer][metalearners.explainer.Explainer] with support for binary classification and regression outcomes and discrete treatment variants.
  • Integration of [Explainer][metalearners.explainer.Explainer] with [MetaLearner][metalearners.metalearner.MetaLearner] for feature importance and SHAP values calculations.
  • Implemented model reuse through the fitted_nuisance_models and fitted_propensity_model parameters of [MetaLearner][metalearners.metalearner.MetaLearner].
  • Allow for fit_params in [MetaLearner.fit][metalearners.metalearner.MetaLearner.fit].

0.2.0 (2024-05-28)

Beta release with:

  • [DRLearner][metalearners.drlearner.DRLearner] with support for binary classification and regression outcomes and discrete treatment variants.
  • Generalization of [TLearner][metalearners.tlearner.TLearner], [XLearner][metalearners.xlearner.XLearner], and [RLearner][metalearners.rlearner.RLearner] to allow for more than two discrete treatment variants.
  • Unification of shapes returned by predict methods.
  • [simplify_output][metalearners.utils.simplify_output] and [metalearner_factory][metalearners.utils.metalearner_factory].

0.1.0 (2024-05-16)

Alpha release with:

  • [TLearner][metalearners.tlearner.TLearner] with support for binary classification and regression outcomes and binary treatment variants.
  • [SLearner][metalearners.slearner.SLearner] with support for binary classification and regression outcomes and discrete treatment variants.
  • [XLearner][metalearners.xlearner.XLearner] with support for binary classification and regression outcomes and binary treatment variants.
  • [RLearner][metalearners.rlearner.RLearner] with support for binary classification and regression outcomes and binary treatment variants.