Most recent releases are shown at the top. Each release shows:
- New: New classes, methods, functions, etc
- Changed: Additional parameters, changes to inputs or outputs, etc
- Fixed: Bug fixes that don't change documented behaviour
- N/A
- migrated from
nbdev1
tonbdev2
- fixed dependency issues with scikit-learn and pandas
- N/A
- updated dependencies
- N/A
- Added
model_name
parameter toCausalBertModel
to support other DistilBert models (e.g., multilingual)
- N/A
- N/A
- Added support for
CausalBert
- Added
p
parameter toCausalInferenceModel.fit
andCausalInferenceModel.predict
for user-supplied propensity scores in X-Learner and R-Learner. - Removed CV from propensity score computations in X-Learner and R-Learner and increase default
max_iter
to 10000
- Resolved problem with
CausalInferenceModel.tune_and_use_default_learner
when outcome is continuous - Changed to
max_iter=10000
for defaultLogisticRegression
base learner
- N/A
- Use
LinearRegression
andLogisticRegression
as default base learners fors-learner
. - changed parameter name of
metalearner_type
tomethod
inCausalInferenceModel
.
- Resolved mis-references in
_balance
method (renamed from_minimize_bias
). - Fixed convergence issues and factored out propensity score computations to
CausalInferenceModel.compute_propensity_scores
.
- N/A
- N/A
- Added
sample_size
parameter toCausalInferenceModel.evalute_robustness
- Added
CausalInferenceModel.evaluate_robustness
method to assess robustness of causal estimates using sensitivity analysis
- reduced dependencies with local metalearner implementations
- N/A
- key driver analysis
CausalInfererenceModel.fit
returnsself
- N/A
- N/A
- N/A
- version fix
- N/A
- Better interpretability and explainability of treatment effects
- Fixes to some bugs in preprocessing
- N/A
- Refactored DataFrame preprocessing
- N/A
- First release.
- N/A
- N/A