ParDAG-class {pcalg}

Class "ParDAG" of Parametric Causal Models

Description

This virtual base class represents a parametric causal model.

Details

The class "ParDAG" serves as a basis for simulating observational and/or interventional data from causal models as well as for parameter estimation (maximum-likelihood estimation) for a given causal model in the presence of a data set with jointly observational and interventional data.

The virtual base class "ParDAG" provides a “skeleton” for all functions relied to the aforementioned task. In practical cases, a user may always choose an appropriate class derived from ParDAG which represents a specific parametric model class. The base class itself does not represent such a model class.

Constructor

new("ParDAG", nodes, in.edges, params)

nodes

Vector of node names; cf. also field .nodes.

in.edges

A list of length p consisting of index vectors indicating the edges pointing into the nodes of the DAG.

params

A list of length p consisting of parameter vectors modeling the conditional distribution of a node given its parents; cf. also field .params.

Fields

.nodes:

Vector of node names; defaults to as.character(1:p), where p denotes the number of nodes (variables) of the model.

.in.edges:

A list of length p consisting of index vectors indicating the edges pointing into the nodes of the DAG.

.params:

A list of length p consisting of parameter vectors modeling the conditional distribution of a node given its parents. The entries of the parameter vectors only get a concrete meaning in derived classes belonging to specific parametric model classes.

Class-Based Methods

node.count():

Yields the number of nodes (variables) of the model.

simulate(n, target, int.level):

Generates n (observational or interventional) samples from the parametric causal model. The intervention target to be used is specified by the parameter target; if the target is empty (target = integer(0)), observational samples are generated. int.level indicates the values of the intervened variables; if it is a vector of the same length as target, all samples are drawn from the same intervention levels; if it is a matrix with n rows and as many columns as target has entries, its rows are interpreted as individual intervention levels for each sample.

edge.count():

Yields the number of edges (arrows) in the DAG.

mle.fit(score):

Fits the parameters using an appropriate Score object.

Methods

plot

signature(x = "ParDAG", y = "ANY"): plots the underlying DAG of the causal model. Parameters are not visualized.

Author(s)

Alain Hauser ([e-mail])

See Also

GaussParDAG