NEWS
CRE 0.2.7 (2024-10-19)
Changed
- The maintainer role has been transitioned from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi.
CRE 0.2.6 (2024-04-21)
Added
- A copy of inTrees package source code.
Removed
- The inTrees package dependency
CRE 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
.
CRE 0.2.4 (2023-06-14)
Changed
- Method paper description is updated.
CRE 0.2.3 (2023-04-27)
Removed
- Bayesian Causal Forest (
bcf
) ITE estimator is not supported.
CRE 0.2.2 (2023-04-17)
Changed
- Fixed failing unit tests on specific operating systems.
CRE 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
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 (2023-01-19)
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 (2022-10-22)
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 (2022-10-18)
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
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.