CovSelHigh: Model-Free Covariate Selection in High Dimensions
Version 1.1.1

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.

Getting started

Package details

AuthorJenny Häggström
Date of publication2017-07-03 09:35:40 UTC
MaintainerJenny Häggström <[email protected]>
Package repositoryView on CRAN
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CovSelHigh documentation built on July 4, 2017, 9:15 a.m.