analytic_linear_gaussian_conditining

Return the mean and the covariance matrix of the conditional distribution of a linear Gaussian SCM

The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.

Juha Karvanen

R6causal

R6 Class for Structural Causal Models

analytic_linear_gaussian_conditining function

<dl><dt>scm</dt>
<dd>A linear Gaussian SCM</dd>
<dt>situation</dt>
<dd>A list with the following element:<ul>
<li>condition : either a string that gives an SQL query ( e.g. "select x,y,z from DATA where" )
or a data.table consisting of the valid rows ( e.g. data.table::data.table( x = 0, y = 0))</li>
</ul></dd></dl>

Arguments

Return the mean and the covariance matrix of the conditional distribution of a linear Gaussian SCM — analytic_linear_gaussian_conditining

<dl>

<dt>scm</dt>
<dd>A linear Gaussian SCM</dd>


<dt>situation</dt>
<dd>A list with the following element:<ul>
<li>condition : either a string that gives an SQL query ( e.g. "select x,y,z from DATA where" )
or a data.table consisting of the valid rows ( e.g. data.table::data.table( x = 0, y = 0))</li>
</ul></dd>

</dl>

analytic_linear_gaussian_conditining: Return the mean and the covariance matrix of the conditional distribution of a linear Gaussian SCM

Description

Usage

Arguments