In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Using Joint Variable Importance Plots to Prioritize Variables in Assessing the Impact of Glyburide on Adverse Birth Outcomes" by Liao et al. (2023) <arXiv:2301.09754>. See the package paper by Liao and Pimentel (2023) <arXiv:2302.10367> for a beginner friendly user introduction.
|Depends:||R (≥ 3.3)|
|Imports:||ggrepel (≥ 0.9.2), ggplot2 (≥ 3.4.0)|
|Suggests:||causaldata, devtools (≥ 2.4.5), knitr, MatchIt, WeightIt, optmatch, optweight (≥ 0.2.4), rmarkdown (≥ 2.18), testthat (≥ 3.0.0)|
|Author:||Lauren D. Liao [aut, cre], Samuel D. Pimentel [aut]|
|Maintainer:||Lauren D. Liao <ldliao at berkeley.edu>|
|License:||MIT + file LICENSE|
|Citation:||jointVIP citation info|
|CRAN checks:||jointVIP results|
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|Windows binaries:||r-devel: jointVIP_0.1.2.zip, r-release: jointVIP_0.1.2.zip, r-oldrel: jointVIP_0.1.2.zip|
|macOS binaries:||r-release (arm64): jointVIP_0.1.2.tgz, r-oldrel (arm64): jointVIP_0.1.2.tgz, r-release (x86_64): jointVIP_0.1.2.tgz, r-oldrel (x86_64): jointVIP_0.1.2.tgz|
|Old sources:||jointVIP archive|
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