get_nodal_types

As type labels are hard to interpret for large models, the type list
includes an attribute to help interpret them.
See <code>attr(types, interpret)</code>

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

get_nodal_types function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>collapse</dt>
<dd>Logical. If `TRUE`, shows unique nodal types for each node.
If `FALSE`, shows for each node a matrix with nodal types as rows and
parent types as columns, if applicable. Defaults to `TRUE`.</dd></dl>

Arguments

Get list of types for nodes in a DAG — get_nodal_types

<dl>

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


<dt>collapse</dt>
<dd>Logical. If `TRUE`, shows unique nodal types for each node.
If `FALSE`, shows for each node a matrix with nodal types as rows and
parent types as columns, if applicable. Defaults to `TRUE`.</dd>

</dl>

Get list of types for nodes in a DAG

get_nodal_types: Get list of types for nodes in a DAG

Description

Usage

Value

Arguments