Class GFci

java.lang.Object
edu.cmu.tetrad.search.GFci
All Implemented Interfaces:
IGraphSearch

public final class GFci extends Object implements IGraphSearch
Implements a modification of FCI that started by running the FGES algorithm and then fixes that result to be correct for latent variables models. First, colliders from the FGES results are copied into the final circle-circle graph, and some independence reasoning is used to add the remaining colliders into the graph. Then, the FCI final orientation rules are applied. The reference is here:

Ogarrio, J. M., Spirtes, P., & Ramsey, J. (2016, August). A hybrid causal search algorithm for latent variable models. In Conference on probabilistic graphical models (pp. 368-379). PMLR.

Because the method both runs FGES (a score-based algorithm) and does additional checking of conditional independencies, both as part of its collider orientation step and also as part of the the definite discriminating path step in the final FCI orientation rules, both a score and a test need to be used to construct a GFCI algorithm.

Note that various score-based algorithms could be used in place of FGES for the initial step; in this repository we give three other options, GRaSP-FCI, BFCI (BOSS FCI), and SP-FCI (see).

For more information on the algorithm, see the reference above.

This class is configured to respect knowledge of forbidden and required edges, including knowledge of temporal tiers.

Version:
$Id: $Id
Author:
Juan Miguel Ogarrio, peterspirtes, josephramsey
See Also: