Changes in version 0.2.7 (2024-10-19) Changed - The maintainer role has been transitioned from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi. Changes in version 0.2.6 (2024-04-21) Added - A copy of inTrees package source code. Removed - The inTrees package dependency Changes in version 0.2.5 (2023-12-06) 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. Changes in version 0.2.4 (2023-06-14) Changed - Method paper description is updated. Changes in version 0.2.3 (2023-04-27) Removed - Bayesian Causal Forest (bcf) ITE estimator is not supported. Changes in version 0.2.2 (2023-04-17) Changed - Fixed failing unit tests on specific operating systems. Changes in version 0.2.1 (2023-03-17) Changed - Replace BATE with ATE in CATE Linear Decomposition. - Update plot() function (remove ATE, old BATE, and explicit AATEs). Added - Code of Conduct. Removed - Causal Tree benchmark in functional tests. Bug fixes - Rank-Deficient Rule Matrix Issue (redundant rules). - Intervention Variables Filtering (ordered filtering). Changes in version 0.2.0 (2023-01-19) 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). Changes in version 0.1.1 (2022-10-22) Changed - binary parameter in generate_cre_dataset -> binary_outcome . - filter_cate hyper-parameter -> t_pvalue. - t_anom hyper-parameter -> t_ext. - effect_modifier hyper-parameter -> intervention_vars. - lasso_rules_filter function -> discover_causal_rules. - split_data function -> honest_splitting. - prune_rules function -> ``filter_irrelevant_rules`. - discard_correlated_rules function -> filter_correlated_rules. - discard_anomalous_rules function -> filter_extreme_rules. Added - Weighted LASSO for Causal Rules Discovery (by penalty_rl hyper-parameter). Changes in version 0.1.0 (2022-10-18) Changed - Update examples and tests for all functions. - q hyper-parameter -> cutoff. - pfer_val hyper-parameter -> pfer. - select_causal_rules function -> lasso_rules_filter. - t hyper-parameter -> t_anom. - Separate standardization, and remove filtering from generate_rules_matrix function. - summary.cre function to describe results. - min_nodes hyper-parameter -> node_size (randomForest convention). - cre returns an S3 object. Added - Examples and tests for all functions. - prune_rules function to discard un-predictive rules. - discard_anomalous_rules function to discard anomalous rules (see t_corr hyper-parameter.). - discard_correlated_rules function to discard correlated rules (see t_anom hyper-parameter). - effect_modifiers parameter in generate_rules function for covariates filtering. - generate_causal_rules function. - Helper function with SuperLearner package for propensity score estimation in estimate_ite_xyz. - Five methods for CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) in estimate_cate function. - (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters to complement SuperLearner package. - cate_method method-parameter to select CATE estimation method. - filter_cate method-parameter for estimation filtering. - p parameter (in generate_cre_dataset function) to set the number of covariates. - replace parameter (in generate_rules function) to allow bootstrapping. - cre.print generic function to print cre S3 object results. - cre.summary generic functions to summarize cre S3 object Results. - 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 to test the functionality of the package. - stability_selection function for causal rules selection. Removed - estimate_ite_blp function. - take1() function. Bug fixes - Undesired 'All' Decision Rule Issue. - No Causal Rule Selected Issue. Changes in version 0.0.1 Changed - estimate_cate include two methods for estimating the CATE values. - cre added initial checks for binary outcome and whether to include the propensity score in the ITE estimation. - estimate_ite_xyz conduct propensity score estimation using helper function. Added - Example for generate_cre_dataset. - set_logger and get_logger. - check_input_data function. - generate_cre_dataset function to generate synthetic data for testing the package. - test-generate_cre_dataset function test. - estimate_ps function to estimate the propensity score. - estimate_ite_xbart function to generate ITE estimates using accelerated BART. - estimate_ite_xbcf function to generate ITE estimates using accelerated BCF. - analyze_sensitivity function to conduct sensitivity analysis for unmeasured confounding. - cre function to perform the entire Causal Rule Ensemble method. - estimate_cate function to generate CATE estimates from the ITE estimates and select rules. - estimate_ite function to generate ITE estimates using the user-specified method (calls the other estimate_ite_xyz functions). - estimate_ite_bart function to generate ITE estimates using BART. - estimate_ite_bcf function to generate ITE estimates using Bayesian Causal Forests. - estimate_ite_cf function to generate ITE estimates using Causal Forests. - estimate_ite_ipw function to generate ITE estimates using IPW. - estimate_ite_or function to generate ITE estimates using Outcome Regression. - estimate_ite_sipw function to generate ITE estimates using SIPW. - extract_rules function to extract a list of causal rules from randomForest and GBM models. - generate_rules function to generate causal rule models using randomForest and GBM methods. - generate_rules_matrix function to convert a list of causal rules into a matrix. - select_causal_rules function to apply penalized regression to causal rules. to select only the most important ones. - split_data function to split input data into discovery and inference subsamples. - take1 function to create a subsample of indices. Removed - seed argument in generate_cre_datase function.