make_parameter_matrix

Calculate parameter matrix assuming no confounding. The parameter matrix maps from parameters into causal types. In models without confounding parameters correspond to nodal types.

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

make_parameter_matrix function

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

Arguments

Make parameter matrix — make_parameter_matrix

<dl>

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

</dl>

Make parameter matrix

make_parameter_matrix: Make parameter matrix

Description

Usage

Value

Arguments

Examples