make_prior_distribution

Create a `n_param`x `n_draws` database of possible lambda draws to be
attached to the model.

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_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

Make a prior distribution from priors — make_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>

Make a prior distribution from priors

make_prior_distribution: Make a prior distribution from priors

Description

Usage

Value

Arguments

See Also

Examples