autoplot.cre | A helper function for cre object |
check_hyper_params | Check input parameters |
check_input_data | Check input data |
check_method_params | Check method-related parameters |
cre | Causal rule ensemble |
CRE-package | The CRE package |
discover_rules | Discover rules |
estimate_cate | Estimate the Conditional Average Treatment Effect |
estimate_ite | Estimate the Individual Treatment Effect (ITE) |
estimate_ite_aipw | Estimate the Individual Treatment Effect (ITE) using... |
estimate_ite_bart | Estimate the Individual Treatment Effect (ITE) using Bayesian... |
estimate_ite_cf | Estimate the Individual Treatment Effect (ITE) using Causal... |
estimate_ite_slearner | Estimate the Individual Treatment Effect (ITE) using... |
estimate_ite_tlearner | Estimate the Individual Treatment Effect (ITE) using... |
estimate_ite_tpoisson | Estimate the Individual Treatment Effect (ITE) using... |
estimate_ite_xlearner | Estimate the Individual Treatment Effect (ITE) using... |
estimate_ps | Estimate the propensity score |
evaluate | Discovery (performance) evaluation |
extract_effect_modifiers | Extract effect modifiers |
extract_rules | Extract (causal) decision rules |
filter_correlated_rules | Filter correlated rules |
filter_extreme_rules | Filter extreme decision rules |
filter_irrelevant_rules | Filter irrelevant decision rules using leave-one-out pruning |
generate_cre_dataset | Generate CRE synthetic data |
generate_rules | Generate rules |
generate_rules_matrix | Generate rules matrix |
get_logger | Get Logger settings |
honest_splitting | Honest splitting |
interpret_rules | Interpret rules |
plot.cre | Extend generic plot functions for cre class |
predict.cre | Predict individual treatment effect via causal rule ensemble |
print.cre | Extend print function for the CRE object |
select_rules | Select rules |
set_logger | Set Logger settings |
standardize_rules_matrix | Standardize Rules Matrix |
summary.cre | Print summary of CRE object |
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