get_estimands

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_estimands function

<dl><dt>jobs</dt>
<dd><code>DataFrame</code> of argument combinations</dd>
<dt>given_types</dt>
<dd>output from <code>queries_to_types</code></dd>
<dt>query_types</dt>
<dd>output from <code>queries_to_types</code></dd>
<dt>type_distributions</dt>
<dd>output from <code>get_type_distributions</code></dd></dl>

Arguments

helper to get estimands — get_estimands

<dl>

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


<dt>given_types</dt>
<dd>output from <code>queries_to_types</code></dd>


<dt>query_types</dt>
<dd>output from <code>queries_to_types</code></dd>


<dt>type_distributions</dt>
<dd>output from <code>get_type_distributions</code></dd>

</dl>

helper to get estimands

get_estimands: helper to get estimands

Description

Usage

Value

Arguments