CovSelHigh: 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) <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.

Getting started

Package details

AuthorJenny Häggström
MaintainerJenny Häggström <>
Package repositoryView on CRAN
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CovSelHigh documentation built on May 2, 2019, 3:25 a.m.