get_param_dist

Using parameters, priors, or posteriors

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

get_param_dist function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>using</dt>
<dd>A character string. Indicates whether to
use `priors`, `posteriors` or `parameters`.</dd>
<dt>n_draws</dt>
<dd>An integer. If no prior distribution is provided,
generate prior distribution with <code>n_draws</code> number of draws.</dd></dl>

Arguments

Get a distribution of model parameters — get_param_dist

<dl>

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


<dt>using</dt>
<dd>A character string. Indicates whether to
use `priors`, `posteriors` or `parameters`.</dd>


<dt>n_draws</dt>
<dd>An integer. If no prior distribution is provided,
generate prior distribution with <code>n_draws</code> number of draws.</dd>

</dl>

Get a distribution of model parameters

get_param_dist: Get a distribution of model parameters

Description

Usage

Value

Arguments

Examples