print.parameters_prior

print method for class <code>parameters_prior</code>.

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

print.parameters_prior function

<dl><dt>x</dt>
<dd>An object of <code>parameters_prior</code> class, which is a sub-object of
an object of the <code>causal_model</code> class produced using
<code>set_prior_distribution</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Print a short summary for causal_model parameter prior distributions — print.parameters_prior

<dl>

<dt>x</dt>
<dd>An object of <code>parameters_prior</code> class, which is a sub-object of
an object of the <code>causal_model</code> class produced using
<code>set_prior_distribution</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

Print a short summary for causal_model parameter prior distributions

print.parameters_prior: Print a short summary for causal_model parameter prior distributions

Description

Usage

Arguments