update_causal_types

Update causal types based on nodal types

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

update_causal_types function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>restrict_given</dt>
<dd>a character vector of subsetting instructions for
rows to be dropped from causal types data.frame.</dd></dl>

Arguments

Update causal types based on nodal types — update_causal_types

<dl>

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


<dt>restrict_given</dt>
<dd>a character vector of subsetting instructions for
rows to be dropped from causal types data.frame.</dd>

</dl>

Update causal types based on nodal types

update_causal_types: Update causal types based on nodal types

Description

Usage

Value

Arguments

Examples