CovSelHigh: Model-Free Covariate Selection in High Dimensions

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Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.

Author
Jenny Häggström
Date of publication
2016-04-26 08:44:06
Maintainer
Jenny Häggström <jenny.haggstrom@umu.se>
License
GPL-3
Version
1.0.0

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Man pages

cov.sel.high
Model-Free Covariate Selection in High Dimensions
cov.sel.high.lasso
cov.sel.high.lasso
cov.sel.high.rf
cov.sel.high.rf
cov.sel.high.sim
Simulate Example Data for CovSelHigh
cov.sel.high.sim.res
Summarize Simulation Results for CovSelHigh

Files in this package

CovSelHigh
CovSelHigh/NAMESPACE
CovSelHigh/R
CovSelHigh/R/cov.sel.high.lasso.R
CovSelHigh/R/cov.sel.high.sim.res.R
CovSelHigh/R/cov.sel.high.R
CovSelHigh/R/cov.sel.high.rf.R
CovSelHigh/R/cov.sel.high.sim.R
CovSelHigh/MD5
CovSelHigh/DESCRIPTION
CovSelHigh/man
CovSelHigh/man/cov.sel.high.sim.res.Rd
CovSelHigh/man/cov.sel.high.Rd
CovSelHigh/man/cov.sel.high.rf.Rd
CovSelHigh/man/cov.sel.high.sim.Rd
CovSelHigh/man/cov.sel.high.lasso.Rd