Model-Free Covariate Selection in High Dimensions

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) and VanderWeele and Shpitser (2011) . Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.


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install.packages("CovSelHigh")

1.1.1 by Jenny Häggström, 2 years ago


Browse source code at https://github.com/cran/CovSelHigh


Authors: Jenny Häggström


Documentation:   PDF Manual  


GPL-3 license


Imports bnlearn, MASS, bindata, Matching, doRNG, glmnet, randomForest, foreach, xtable, doParallel, bartMachine, tmle


See at CRAN