make_parmap

Generates a matrix with a row per parameter and a column per data type.

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_parmap function

<dl><dt>model</dt>
<dd>A <code>causal_model</code>. A model object generated by
<code>make_model</code>.</dd>
<dt>A</dt>
<dd>A <code>data.frame</code>. Ambiguity matrix. Not required but may be
provided to avoid repeated computation for simulations.</dd>
<dt>P</dt>
<dd>A <code>data.frame</code>. Parameter matrix. Not required but may be
provided to avoid repeated computation for simulations.</dd></dl>

Arguments

Make parmap: a matrix mapping from parameters to data types — make_parmap

<dl>

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


<dt>A</dt>
<dd>A <code>data.frame</code>. Ambiguity matrix. Not required but may be
provided to avoid repeated computation for simulations.</dd>


<dt>P</dt>
<dd>A <code>data.frame</code>. Parameter matrix. Not required but may be
provided to avoid repeated computation for simulations.</dd>

</dl>

Make parmap: a matrix mapping from parameters to data types

make_parmap: Make parmap: a matrix mapping from parameters to data types

Description

Usage

Value

Arguments