var_in_query

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

var_in_query 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></dl>

Arguments

List of nodes contained in query — var_in_query

<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>

</dl>

List of nodes contained in query

var_in_query: List of nodes contained in query

Description

Usage

Value

Arguments