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max_rules
hyper parameters for max rules filtering.B
hyper-parameter,subsample
hyper-parameter.rules
(implicit form) in cre() function return.stability_selection
binary -> string (‘no’,‘vanilla’,‘error_control’).ntrees_gbm
hyper-parameter and ntrees_gbm
hyper-parameter in ntrees
hyper-parameter.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
.max_nodes
hyper-parameter.replace
hyper-parameter.penalty_rl
hyper-parameter.t_pvalue
hyper-parameter.ite_pred
from cre() function return.intervention_vars
.bcf
) ITE estimator is not supported.plot()
function (remove ATE, old BATE, and explicit AATEs).offset
method-parameter -> hyper-parameterestimate_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
.cre
.cre
object: added parameters and ite estimation.generate_cre_dataset
).slearner
) method for ITE estimation.tlearner
) method for ITE estimation.xlearner
) method for ITE estimation.summary.cre
.verbose
parameter in summary.cre
.ite
, additional cre
input parameter to use personalized ite estimations.estimation.R
).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.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.type_decay
hyper-parameter.linreg
for CATE estimation (remove cate_method
and cate_SL_library
parameters).method_params
and hyper_params
additional parameters in summary.cre
.random_state
parameter.include_offset
method parameter.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
.penalty_rl
hyper-parameter).q
hyper-parameter -> cutoff
.pfer_val
hyper-parameter -> pfer
.select_causal_rules
function -> lasso_rules_filter
.t
hyper-parameter -> t_anom
.generate_rules_matrix
function.summary.cre
function to describe results.min_nodes
hyper-parameter -> node_size
(randomForest
convention).cre
returns an S3 object.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.SuperLearner
package for propensity score estimation in estimate_ite_xyz
.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.estimate_ite_blp
function.take1()
function.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.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.seed
argument in generate_cre_datase
function.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.