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.
| 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 |
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