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Changelog

Source: NEWS.md

CRE 0.2.5 (2023-12-6)

CRAN release: 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.

CRE 0.2.4 (2023-6-14)

CRAN release: 2023-06-14

Changed

  • Method paper description is updated.

CRE 0.2.3 (2023-4-27)

CRAN release: 2023-04-27

Removed

  • Bayesian Causal Forest (bcf) ITE estimator is not supported.

CRE 0.2.2 (2023-4-17)

CRAN release: 2023-04-17

Changed

  • Fixed failing unit tests on specific operating systems.

CRE 0.2.1 (2023-3-17)

CRAN release: 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).

CRE 0.2.0 (2023-1-19)

CRAN release: 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).

CRE 0.1.1 (2022-10-18)

CRAN release: 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).

CRE 0.1.0 (2022-10-17)

CRAN release: 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.

CRE 0.0.1 (2021-10-20)

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.