Package: medoutcon 0.2.2

Nima Hejazi

medoutcon: Efficient Natural and Interventional Causal Mediation Analysis

Efficient estimators of interventional (in)direct effects in the presence of mediator-outcome confounding affected by exposure. The effects estimated allow for the impact of the exposure on the outcome through a direct path to be disentangled from that through mediators, even in the presence of intermediate confounders that complicate such a relationship. Currently supported are non-parametric efficient one-step and targeted minimum loss estimators based on the formulation of Díaz, Hejazi, Rudolph, and van der Laan (2020) <doi:10.1093/biomet/asaa085>. Support for efficient estimation of the natural (in)direct effects is also provided, appropriate for settings in which intermediate confounders are absent. The package also supports estimation of these effects when the mediators are measured using outcome-dependent two-phase sampling designs (e.g., case-cohort).

Authors:Nima Hejazi [aut, cre, cph], Iván Díaz [aut], Kara Rudolph [aut], Philippe Boileau [ctb], Mark van der Laan [ctb, ths]

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NEWS

# Install 'medoutcon' in R:
install.packages('medoutcon', repos = c('https://ictml-project.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/nhejazi/medoutcon/issues

On CRAN:

causal-inferencecausal-machine-learninginverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects

4.34 score 13 stars 17 scripts 1 exports 124 dependencies

Last updated 8 months agofrom:e2d3fd8a64. Checks:OK: 7. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 28 2024
R-4.5-winOKOct 28 2024
R-4.5-linuxOKOct 28 2024
R-4.4-winOKOct 28 2024
R-4.4-macOKOct 28 2024
R-4.3-winOKOct 28 2024
R-4.3-macOKOct 28 2024

Exports:medoutcon

Dependencies:abindassertthatbackportsbase64encBBmiscbitopsbslibcachemcaretcaToolscheckmateclasscliclockcodetoolscolorspacecpp11crayondata.tabledelayeddiagramdigestdplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2glm2globalsgluegowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivorigamiparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrmarkdownROCRrpartrstackdequesassscalesshapesl3SQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8uuidvctrsviridisLitevisNetworkwithrxfunyamlzeallot

Efficient causal mediation analysis with the natural and interventional effects

Rendered fromintro_medoutcon.Rmdusingknitr::rmarkdownon Oct 28 2024.

Last update: 2024-03-04
Started: 2019-04-07