This function fits a potentially sparse supervised learning model and returns
a bit vector indicating which features were selected.

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

l0 function

<dl><dt>x</dt>
<dd>Design matrix.</dd>
<dt>y</dt>
<dd>Outcome vector.</dd>
<dt>s</dt>
<dd>Regression method. Current options are <code>"lasso"</code> or
<code>"boost"</code>.</dd>
<dt>params</dt>
<dd>Optional list of parameters to use when <code>s = "boost"</code>.</dd>
<dt>...</dt>
<dd>Extra parameters to be passed to the feature selection subroutine.</dd></dl>

Arguments

Feature selection subroutine — l0

<dl>

<dt>x</dt>
<dd>Design matrix.</dd>


<dt>y</dt>
<dd>Outcome vector.</dd>


<dt>s</dt>
<dd>Regression method. Current options are <code>"lasso"</code> or
<code>"boost"</code>.</dd>


<dt>params</dt>
<dd>Optional list of parameters to use when <code>s = "boost"</code>.</dd>


<dt>...</dt>
<dd>Extra parameters to be passed to the feature selection subroutine.</dd>

</dl>

l0: Feature selection subroutine

Description

Usage

Arguments