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)