set_parmap

Generates and adds parmap to a 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

set_parmap function

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

Arguments

Set parmap: a matrix mapping from parameters to data types — set_parmap

<dl>

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

</dl>

Set parmap: a matrix mapping from parameters to data types

set_parmap: Set parmap: a matrix mapping from parameters to data types

Description

Usage

Value

Arguments

Examples