get_type_distributions

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_type_distributions function

<dl><dt>jobs</dt>
<dd><code>DataFrame</code> of argument combinations</dd>
<dt>model</dt>
<dd>a list of models</dd>
<dt>n_draws</dt>
<dd>integer specifying number of draws from prior distribution</dd>
<dt>parameters</dt>
<dd>optional list of parameter vectors</dd></dl>

Arguments

helper to get type distributions — get_type_distributions

<dl>

<dt>jobs</dt>
<dd><code>DataFrame</code> of argument combinations</dd>


<dt>model</dt>
<dd>a list of models</dd>


<dt>n_draws</dt>
<dd>integer specifying number of draws from prior distribution</dd>


<dt>parameters</dt>
<dd>optional list of parameter vectors</dd>

</dl>

helper to get type distributions

get_type_distributions: helper to get type distributions

Description

Usage

Value

Arguments