v0.12.0b5

This is a beta preparing for our next major release, but does not contain any new user-facing features.

v0.12.0b4

This is a beta preparing for our next major release, but does not contain any new user-facing features.

v0.12.0b3

This is a beta preparing for our next major release, but does not contain any new user-facing features.

v0.12.0b2

This is a beta preparing for our next major release, but does not contain any new user-facing features.

v0.12.0b1

This is a beta preparing for our next major release, but does not contain any new user-facing features.

v0.11.1

This is a minor bugfix release. Changes include:

• A fix to forest tuning (#462)
• A new notebook (#466)
• Miscellaneous minor fixes to documentation and code (#468)

v0.11.0

This is a minor release which:

• Extends support for weighting samples to allow both fractional sample weights as well as frequency weights (#439)
• Fixes some problems with the multi-investment case study and improved policy learners (#441)
• Adds a notebook which uses EconML to estimate treatment effects using the dataset from LaLonde (#448)
• Enables pandas dataframes to be used with CausalForestDML, including tuning (#447)
• Fixes a few other miscellaneous issues (#458, #459)

v0.10.0

This release contains a few new features:

• Introduces new classes for policy learning (see DRPolicyTree and DRPolicyForest in our documentation) (#377)
• Exposes the entire set of nuisance models and scores from training when using multiple monte carlo iterations for ortho-learner subclasses (previously only the final ones were kept) (#433)

It also fixes an interoperability issue with DoWhy (#434). Note that this change also removes the deprecated n_splits argument to our estimators, which had already been renamed to cv for the past several releases.

v0.9.2

This is a minor release that adds the following features:

• Enables easy hyperparameter tuning for CausalForestDML (#390)
• Enables CausalForestDML to compute doubly-robust estimates of the ATE and ATT (#391)

v0.9.1

This is primarily a bugfix release; it has the following improvements:

• Reenable using scikit-learn > 0.22.0 but < 0.24.0 (#422)
• Add more robustness to use of feature names with transformers with inconsitent APIs (#422)
• Provide more precise ATE confidence intervals for linear final models (#418)
• A few small miscellaneous improvements (#419)