An example of 10 variables simulated from a linear SEM

datasets

Estimate a total causal effect from observational data under
linearity and causal sufficiency. The observational data is supposed to
be generated from a linear structural equation model (SEM) with independent
and additive noise. The underlying causal DAG associated the SEM is required
to be known up to a maximally oriented partially directed graph (MPDAG),
which is a general class of graphs consisting of both directed and
undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such
graphs are usually obtained with structure learning algorithms with added
background knowledge. The program is able to estimate every identified
effect, including single and multiple treatment variables. Moreover, the
resulting estimate has the minimal asymptotic covariance (and hence
shortest confidence intervals) among all estimators that are based on the
sample covariance.

Richard Guo

eff2

Efficient Least Squares for Total Causal Effects

ex1 function

A list containing:<dl>
<dt>x</dt>
<dd>treatment variables</dd><dt>y</dt>
<dd>outcome variable</dd><dt>true.effects</dt>
<dd>the true total effect of x on y</dd><dt>B</dt>
<dd>the coefficient matrix of the SEM</dd><dt>amat.dag</dt>
<dd>the adjacency matrix of the causal DAG</dd><dt>amat.cpdag</dt>
<dd>the adjacency matrix of the CPDAG of the causal DAG,
representing the Markov equivalence class of the DAG.</dd><dt>data</dt>
<dd>500 iid samples generated under student-t errors</dd>
</dl>

Format

An example of 10 variables simulated from a linear SEM — ex1

A list containing:<dl>
<dt>x</dt>
<dd>treatment variables</dd>

<dt>y</dt>
<dd>outcome variable</dd>

<dt>true.effects</dt>
<dd>the true total effect of x on y</dd>

<dt>B</dt>
<dd>the coefficient matrix of the SEM</dd>

<dt>amat.dag</dt>
<dd>the adjacency matrix of the causal DAG</dd>

<dt>amat.cpdag</dt>
<dd>the adjacency matrix of the CPDAG of the causal DAG,
representing the Markov equivalence class of the DAG.</dd>

<dt>data</dt>
<dd>500 iid samples generated under student-t errors</dd>


</dl>

ex1: An example of 10 variables simulated from a linear SEM

Description

Usage

Arguments

Format