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Package: medshift 0.1.4

Nima Hejazi

medshift: Causal mediation analysis for stochastic interventions

Estimators of a parameter arising in the decomposition of the population intervention (in)direct effect of stochastic interventions in causal mediation analysis, including efficient one-step, targeted minimum loss (TML), re-weighting (IPW), and substitution estimators. The parameter estimated constitutes a part of each of the population intervention (in)direct effects. These estimators may be used in assessing population intervention (in)direct effects under stochastic treatment regimes, including incremental propensity score interventions and modified treatment policies. The methodology was first discussed by I Díaz and NS Hejazi (2020) <doi:10.1111/rssb.12362>.

Authors:Nima Hejazi [aut, cre, cph], Iván Díaz [aut], Mark van der Laan [ctb, ths], Jeremy Coyle [ctb]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
medshift/json (API)

# Install 'medshift' in R:

install.packages('medshift', repos = c('https://ictml-project.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

causal-inference inverse-probability-weights machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

3.74 score 10 stars 11 scripts 7 exports 122 dependencies

Last updated from:0ae0572fc5. Checks:7 WARNING, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	WARNING	277
source / vignettes	OK	298
linux-release-x86_64	WARNING	270
macos-release-arm64	WARNING	211
macos-oldrel-arm64	WARNING	229
windows-devel	WARNING	253
windows-release	WARNING	239
windows-oldrel	WARNING	252
wasm-release	OK	150

Exports:LF_ipsi medshift Param_medshift pide test_de tmle_medshift tmle3_Spec_medshift

Dependencies:abind assertthat backports base64enc BBmisc bitops bslib cachem caret caTools checkmate class cli clock codetools cpp11 crayon data.table delayed diagram digest dplyr e1071 evaluate farver fastmap fontawesome foreach fs future future.apply generics ggplot2 globals glue gower gplots gtable gtools hardhat highr hms htmltools htmlwidgets igraph ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix memoise mime ModelMetrics mvtnorm nlme nnet numDeriv origami parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr R.methodsS3 R.oo R.utils R6 rappdirs rbibutils RColorBrewer Rcpp Rdpack recipes reshape2 rlang rmarkdown ROCR rpart rstackdeque S7 sass scales shape sl3 sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tinytex tmle3 tzdb utf8 uuid vctrs viridisLite visNetwork withr xfun yaml

Causal Mediation Analysis for Stochastic Interventions

Background | Decomposing the population intervention effect | Estimating the effect decomposition term | Estimating the direct effect | References

Rendered fromintro_medshift.Rmdusingknitr::rmarkdown

Last update: 2022-01-07
Started: 2019-01-05

Help page	Topics
Confidence Intervals for Stochastic Mediation Parameters	confint.medshift
Inverse probability weighted (IPW) estimator	est_ipw
Efficient One-Step Estimator	est_onestep
Substitution estimator	est_substitution
Fit propensity score regression while conditioning on mediators	fit_e_mech
Fit propensity score with incremental stochastic shift intervention	fit_g_mech
Fit outcome regression	fit_m_mech
Fit intervention-specific exponential tilt nuisance parameter	fit_phi_mech
Likelihood Factor for Incremental Propensity Score Interventions	LF_ipsi
Nonparametric estimation of the population intervention (in)direct effects	medshift
Parameter for the Population Intervention (In)direct Effects	Param_medshift
One-step or TML estimation of the population intervention direct effect	pide
Print Method for Class medshift	print.medshift
Summary for Stochastic Mediation Parameter Objects	summary.medshift
Hypothesis test of direct effect with mediated stochastic interventions using the multiplier bootstrap	test_de
TML Estimator for the Counterfactual Mean of a Joint Stochastic Intervention Defining the Population Intervention (In)direct Effects	tmle_medshift
TML Estimator for the Counterfactual Mean of a Joint Stochastic Intervention Defining the Population Intervention (In)direct Effects	tmle3_Spec_medshift

Package: medshift 0.1.4

medshift: Causal mediation analysis for stochastic interventions

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