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.
| Version: | 1.1.1 |
| Depends: | R (≥ 2.14.0) |
| Imports: | bnlearn, MASS, bindata, Matching, doRNG, glmnet, randomForest, foreach, xtable, doParallel, bartMachine, tmle |
| Published: | 2017-07-03 |
| Author: | Jenny Häggström |
| Maintainer: | Jenny Häggström <jenny.haggstrom at umu.se> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | CovSelHigh results |
| Reference manual: | CovSelHigh.pdf |
| Package source: | CovSelHigh_1.1.1.tar.gz |
| Windows binaries: | r-devel: CovSelHigh_1.1.1.zip, r-release: CovSelHigh_1.1.1.zip, r-oldrel: CovSelHigh_1.1.1.zip |
| macOS binaries: | r-release: CovSelHigh_1.1.1.tgz, r-oldrel: CovSelHigh_1.1.1.tgz |
| Old sources: | CovSelHigh archive |
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