EScvtmle: Experiment-Selector CV-TMLE for Integration of Observational and RCT Data

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.

Version: 0.0.1
Depends: R (≥ 4.2), SuperLearner (≥ 2.0.28)
Imports: origami (≥ 1.0.5), dplyr (≥ 1.0.8), tidyselect (≥ 1.2.0), MASS (≥ 7.3.54), stringr (≥ 1.4.0), ggplot2 (≥ 3.3.6), gridExtra (≥ 2.3)
Suggests: testthat (≥ 3.0.0), knitr
Published: 2022-11-02
Author: Lauren Eyler Dang [cre, aut], Maya Petersen [aut], Mark van der Laan [aut]
Maintainer: Lauren Eyler Dang <lauren.eyler at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: EScvtmle results


Reference manual: EScvtmle.pdf


Package source: EScvtmle_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
macOS binaries: r-release (arm64): EScvtmle_0.0.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): EScvtmle_0.0.1.tgz, r-oldrel (x86_64): not available


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