expand_nodal_expression

internal

Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands. Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).

Till Tietz

CausalQueries

Make, Update, and Query Binary Causal Models

Clara Bicalho

Jasper Cooper

Macartan Humphreys

Alan Jacobs

Merlin Heidemanns

Lily Medina

Julio Solis

Georgiy Syunyaev

expand_nodal_expression function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>query</dt>
<dd>A character string. An expression defining nodal types to
interrogate <code>realise_outcomes</code>. An expression of the form
"Y[X=1]" asks for the value of Y when X is set to 1</dd>
<dt>node</dt>
<dd>A character string. The quoted name of a node.</dd>
<dt>join_by</dt>
<dd>A logical operator. Used to connect causal statements:
AND ('&amp;') or OR ('|'). Defaults to '|'.</dd></dl>

Arguments

Helper to expand nodal expression — expand_nodal_expression

<dl>

<dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>


<dt>query</dt>
<dd>A character string. An expression defining nodal types to
interrogate <code>realise_outcomes</code>. An expression of the form
"Y[X=1]" asks for the value of Y when X is set to 1</dd>


<dt>node</dt>
<dd>A character string. The quoted name of a node.</dd>


<dt>join_by</dt>
<dd>A logical operator. Used to connect causal statements:
AND ('&amp;') or OR ('|'). Defaults to '|'.</dd>

</dl>

Helper to expand nodal expression

expand_nodal_expression: Helper to expand nodal expression

Description

Usage

Value

Arguments