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 nondescendants 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 socalled "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.
Package details 


Author  David Watson [aut, cre] (<https://orcid.org/0000000196322159>) 
Maintainer  David Watson <david.s.watson11@gmail.com> 
License  GPL (>= 3) 
Version  0.1.3 
URL  https://github.com/dswatson/cbl 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.