cbl: Causal Discovery under a Confounder Blanket

Methods for learning causal relationships among a set of foreground variables X based on signals from a (potentially much larger) set of background variables Z, which are known non-descendants of X. The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) <doi:10.48550/arXiv.2205.05715>.

Version:	0.1.3
Depends:	R (≥ 3.5.0)
Imports:	data.table, foreach, glmnet, lightgbm
Published:	2022-12-20
DOI:	10.32614/CRAN.package.cbl
Author:	David Watson [aut, cre]
Maintainer:	David Watson <david.s.watson11 at gmail.com>
License:	GPL (≥ 3)
URL:	https://github.com/dswatson/cbl
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	cbl results

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