isIdentified

The total causal effect from x to y is identified if and only if there is
no possibly causal path from x to y that starts with an undirected edge.

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

isIdentified function

<dl><dt>amat</dt>
<dd>adjacency matrix. See <code>estimateEffect</code>
for its coding.</dd>
<dt>x</dt>
<dd>(integer) positions of treatment variables in the adjacency matrix</dd>
<dt>y</dt>
<dd>(integer) positions of outcome variables in the adjacency matrix</dd>
<dt>type</dt>
<dd>string specifying the type of graph of <code>amat</code>. It can be
DAG (<code>type='dag'</code>) or MPDAG/CPDAG (<code>type='pdag'</code>).</dd></dl>

Arguments

Check if a total causal effect is identified — isIdentified

<dl>

<dt>amat</dt>
<dd>adjacency matrix. See <code>estimateEffect</code>
for its coding.</dd>


<dt>x</dt>
<dd>(integer) positions of treatment variables in the adjacency matrix</dd>


<dt>y</dt>
<dd>(integer) positions of outcome variables in the adjacency matrix</dd>


<dt>type</dt>
<dd>string specifying the type of graph of <code>amat</code>. It can be
DAG (<code>type='dag'</code>) or MPDAG/CPDAG (<code>type='pdag'</code>).</dd>

</dl>

isIdentified: Check if a total causal effect is identified

Description

Usage

Value

Arguments

References

See Also

Examples