queries_to_types

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

queries_to_types function

<dl><dt>jobs</dt>
<dd><code>DataFrame</code> of argument combinations</dd>
<dt>model</dt>
<dd>a list of models</dd>
<dt>query_col</dt>
<dd>string specifying the name of the column in jobs
holding queries to be evaluated</dd>
<dt>realisations</dt>
<dd>list of <code>DataFrame</code> outputs from calls
to <code>realise_outcomes</code></dd></dl>

Arguments

helper to get types from queries — queries_to_types

<dl>

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


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


<dt>query_col</dt>
<dd>string specifying the name of the column in jobs
holding queries to be evaluated</dd>


<dt>realisations</dt>
<dd>list of <code>DataFrame</code> outputs from calls
to <code>realise_outcomes</code></dd>

</dl>

helper to get types from queries

queries_to_types: helper to get types from queries

Description

Usage

Value

Arguments