get_prior_distribution

Access the prior distribution from the model if one has been added via `set_prior_distribution`.
Otherwise call `make_prior_distribution` to generate and return an `n_draws x n_param` prior distribution.

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

get_prior_distribution function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>n_draws</dt>
<dd>A scalar. Number of draws.</dd></dl>

Arguments

Get a prior distribution from model — get_prior_distribution

<dl>

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


<dt>n_draws</dt>
<dd>A scalar. Number of draws.</dd>

</dl>

Get a prior distribution from model

get_prior_distribution: Get a prior distribution from model

Description

Usage

Value

Arguments

See Also