minD

Compute the min-D factor of Shah &amp; Samworth's Eq. 8 (2013). Code taken
verbatim from Rajen Shah's personal website:
<a href="http://www.statslab.cam.ac.uk/~rds37/papers/r_concave_tail.R">http://www.statslab.cam.ac.uk/~rds37/papers/r_concave_tail.R</a>.

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) <arXiv:2205.05715>.

David Watson

Causal Discovery under a Confounder Blanket

minD function

<dl><dt>theta</dt>
<dd>Low rate threshold.</dd>
<dt>B</dt>
<dd>Number of complementary pairs for stability selection.</dd>
<dt>r</dt>
<dd>Of r-concavity fame.</dd></dl>

Arguments

Compute the min-D factor of Shah &amp; Samworth's Eq. 8 (2013). Code taken
verbatim from Rajen Shah's personal website:
<a href='http://www.statslab.cam.ac.uk/~rds37/papers/r_concave_tail.R'>http://www.statslab.cam.ac.uk/~rds37/papers/r_concave_tail.R</a>.

CPSS upper bound — minD

<dl>

<dt>theta</dt>
<dd>Low rate threshold.</dd>


<dt>B</dt>
<dd>Number of complementary pairs for stability selection.</dd>


<dt>r</dt>
<dd>Of r-concavity fame.</dd>

</dl>

minD: CPSS upper bound

Description

Usage

Arguments