Release ver0.2.7

• The maintainer role has been transitioned from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi.

v0.2.6

• Dropped direct import of the inTrees package.

v0.2.5

Added

• Add (vanilla) Stability Selection (without Error Control).
• max_rules hyper parameters for max rules filtering.
• Uncertainty Quantification in estimation by bootstrapping.
• B hyper-parameter,
• subsample hyper-parameter.
• rules(implicit form) in cre() function return.
• predict() function for ITE estimation via CRE.

Changed

• Type stability_selection binary -> string ('no','vanilla','error_control').
• Unify ntrees_gbm hyper-parameter and ntrees_gbm hyper-parameter in
  ntrees hyper-parameter.
• In rules generation retrieve decision rules also from internal nodes, and not
  just from terminal nodes.
• ite_method_dis, ite_method_inf method-parameter -> ite_method.
• ps_method_dis, ps_method_inf method-parameter -> learner_ps.
• oreg_method_dis, oreg_method_inf method-parameter -> learner_y.

Removed

• max_nodes hyper-parameter.
• Remove rules generation by Generalized Boosted Regression.
• replace hyper-parameter.
• penalty_rl hyper-parameter.
• t_pvalue hyper-parameter.
• ite_pred from cre() function return.

Bug fixes

• Error saving covariates name in CRE result when using intervention_vars.

v0.2.4

• Update description

v0.2.3

• The inclusion of the bcf package has been discontinued.

v0.2.2

• Fixed failing unit tests on specific operating systems.

v0.2.1

• Replaces BATE with ATE in CATE Linear Decomposition.
• Updates plot() function (remove ATE, old BATE, and explicit AATEs).
• Adds Code of Conduct.

v0.2.0

Changed

• offset method-parameter -> hyper-parameter
• estimate_ite_poisson function -> estimate_ite_tpoisson
• max_dacay hyper-parameter -> t_decay.
• interpret_select_rules function -> interpret_rules.
• generate_causal_rules function -> discover_rules.
• discover_causal_rules function ->select_rules.
• offset_name method parameter -> offset.
• Hyper and method parameters are no more required arguments for cre.
• cre object: added parameters and ite estimation.

Added

• Synthetic data set with 1 or 3 rules (generate_cre_dataset).
• S-Learner (slearner) method for ITE estimation.
• T-Learner (tlearner) method for ITE estimation.
• X-Learner (xlearner) method for ITE estimation.
• Rules Selection description in summary.cre.
• verbose parameter in summary.cre.
• ite, additional cre input parameter to use personalized ite
  estimations.
• Default values for hyper parameters.
• Default values for method parameters.
• Simulation experiments for estimation (estimation.R).
• Simulation experiments for discovery (discovery.R).
• extract_effect_modifiers function (utility for performance evaluation).
• evaluate function for discovery evaluation.
• confounding parameter in generate_cre_dataset to set confounding type.
• ite_pred and model in CRE results.
• binary_covariates parameter in generate_cre_dataset to set covariates
  domain.

Removed

• include_ps_inf method-parameter.
• include_ps_dis method-parameter.
• oreg method for ITE estimation.
• ipw method for ITE estimation.
• sipw method for ITE estimation.
• ITE standard deviation estimation.
• type_decay hyper-parameter.
• Keep only linreg for CATE estimation (remove cate_method and
  cate_SL_library parameters).
• method_params and hyper_params additional parameters in summary.cre.
• ite standardization for Rules Generation.
• random_state parameter.
• include_offset method parameter.

Bug fixes

• Rules Generation Issue (set rules length and fix bootstrapping).

v0.1.0

Changed

• select_causal_rules() is now lasso_rules_filter()
• rules generation now accepts replace parameter to set replacement in bootstrapping
• rename parameter t with t_anom
• add parameter t_corr discard correlation threshold
• define discard_anomalous_rules() and discard_corre_rules() functions and
  and relative tests
• reorganize generate_rules_matrix() (separate standardization, and remove filtering)
• explicit prune_rules() function and add relative tests
• remove take1() function for random Rule Selection
• add effect modifiers filter for Rule Generation
• add generate_causal_rules() function and relative tests
• solve Undesired 'All' Decision Rule Issue
• solve No Causal Rule Selected Issue
• improve cre.summary() function
• min_nodes --> node_size (following the randomForest convention)
• estimate_cate include five methods for estimating the CATE values (poisson, DRLearner, bart-baggr, cf-means, linreg)
• cre added new arguments to (1) complement SuperLearner package (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) and to (2) select CATE method and (3) whether to filter CATE p-values (cate_method and filter_cate).
  Now returns an S3 object.
• estimate_ite_xyz conduct propensity score estimation using helper function with SuperLearner package
• generate_cre_dataset make number of covariates an argument of the function
• improve examples and update tests for all functions

Added

• print and summary generic functions.
• check_input function to isolate input checks.
• estimate_ite_aipw function for augmented inverse propensity weighting
• plot.cre generic function to plot CRE S3 object Results
• test-cre_functional.R tests the functionality of the package
• stability_selection function for causal rules selection

Removed

• estimate_ite_blp function

ver0.0.1

Merge pull request #27 from fasrc/release_v0.0.1

Release v0.0.1