r.TailProbs

Compute the tail probability of an r-concave random variable. 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

r.TailProbs function

<dl><dt>eta</dt>
<dd>Upper bound on the expectation of the r-concave random variable.</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 tail probability of an r-concave random variable. 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 utility functions — r.TailProbs

<dl>

<dt>eta</dt>
<dd>Upper bound on the expectation of the r-concave random variable.</dd>


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


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

</dl>

r.TailProbs: CPSS utility functions

Description

Usage

Arguments