ss_fn

Infer causal direction using stability selection

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

ss_fn function

<dl><dt>df</dt>
<dd>Table of (de)activation rates.</dd>
<dt>epsilon</dt>
<dd>Consistency lower bound, as computed by <code>epsilon_fn</code>.</dd>
<dt>order</dt>
<dd>Causal order of interest, either <code>"ij"</code> or <code>"ji"</code>.</dd>
<dt>rule</dt>
<dd>Inference rule, either <code>"R1"</code> or <code>"R2"</code>.</dd>
<dt>B</dt>
<dd>Number of complementary pairs to draw for stability selection.</dd></dl>

Arguments

Infer causal direction using stability selection — ss_fn

<dl>

<dt>df</dt>
<dd>Table of (de)activation rates.</dd>


<dt>epsilon</dt>
<dd>Consistency lower bound, as computed by <code>epsilon_fn</code>.</dd>


<dt>order</dt>
<dd>Causal order of interest, either <code>"ij"</code> or <code>"ji"</code>.</dd>


<dt>rule</dt>
<dd>Inference rule, either <code>"R1"</code> or <code>"R2"</code>.</dd>


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

</dl>

ss_fn: Infer causal direction using stability selection

Description

Usage

Arguments