clean_param_vector

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

clean_param_vector function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>parameters</dt>
<dd>A vector of real numbers in [0,1]. Values of parameters to
specify (optional). By default, parameters is drawn from
<code>model$parameters_df</code>.</dd></dl>

Arguments

Clean parameter vector — clean_param_vector

<dl>

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


<dt>parameters</dt>
<dd>A vector of real numbers in [0,1]. Values of parameters to
specify (optional). By default, parameters is drawn from
<code>model$parameters_df</code>.</dd>

</dl>

Clean parameter vector

clean_param_vector: Clean parameter vector

Description

Usage

Value

Arguments