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drtmle (version 1.1.2)

Doubly-Robust Nonparametric Estimation and Inference

Description

Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), ; MJ van der Laan (2014), ).

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Install

install.packages('drtmle')

Monthly Downloads

262

Version

1.1.2

License

MIT + file LICENSE

Issues

4

Pull Requests

0

Stars

20

Forks

7

Maintainer

David Benkeser

Last Published

January 5th, 2023

Functions in drtmle (1.1.2)

estimateG

estimateG
adaptive_iptw

Compute asymptotically linear IPTW estimators with super learning for the propensity score
ci

Compute confidence intervals for drtmle and adaptive_iptw@
ci.adaptive_iptw

Confidence intervals for adaptive_iptw objects
average_est_cov_list

Helper function for averaging lists of estimates generated in the main for loop of drtmle
eval_Dstar

Evaluate usual efficient influence function
average_ic_list

Helper function to average convergence results and drtmle influence function estimates over multiple fits
ci.drtmle

Confidence intervals for drtmle objects
eval_Dstar_Q

Evaluate extra piece of efficient influence function resulting from misspecification of propensity score
estimateQ

estimateQ
eval_Diptw

Evaluate usual influence function of IPTW
plot.drtmle

Plot reduced dimension regression fits
estimateG_loop

estimateG_loop
estimategrn

estimategrn
print.ci.drtmle

Print the output of ci.drtmle
estimategrn_loop

estimategrn_loop
print.drtmle

Print the output of a "drtmle" object.
wald_test.adaptive_iptw

Wald tests for adaptive_iptw objects
estimateQ_loop

estimateQ_loop
predict.SL.npreg

Predict method for SL.npreg
wald_test.drtmle

Wald tests for drtmle objects
fluctuateQ1

fluctuateQ1
drtmle

TMLE estimate of the average treatment effect with doubly-robust inference
reorder_list

Helper function to reorder lists according to cvFolds
make_validRows

Make list of rows in each validation fold.
estimateQrn

estimateQrn
estimateQrn_loop

estimateQrn_loop
fluctuateQ2

fluctuateQ2
tmp_method.CC_LS

Temporary fix for convex combination method mean squared error Relative to existing implementation, we reduce the tolerance at which we declare predictions from a given algorithm the same as another
eval_Diptw_g

Evaluate extra piece of the influence function for the IPTW
partial_cv_preds

Helper function to properly format partially cross-validated predictions from a fitted super learner.
eval_Dstar_g

Evaluate extra piece of efficient influence function resulting from misspecification of outcome regression
extract_models

Help function to extract models from fitted object
print.adaptive_iptw

Print the output of a "adaptive_iptw" object.
print.ci.adaptive_iptw

Print the output of ci.adaptive_iptw
fluctuateG

fluctuateG
tmp_method.CC_nloglik

Temporary fix for convex combination method negative log-likelihood loss Relative to existing implementation, we reduce the tolerance at which we declare predictions from a given algorithm the same as another. Note that because of the way SuperLearner is structure, one needs to install the optimization software separately.
wald_test

Wald tests for drtmle and adaptive_iptw objects
fluctuateQ

fluctuateQ
print.wald_test.adaptive_iptw

Print the output of wald_test.adaptive_iptw
print.wald_test.drtmle

Print the output of wald_test.drtmle
SL.npreg

Super learner wrapper for kernel regression