udag2pdag: Last PC Algorithm Step: Extend Object with Skeleton to...

In pcalg: Methods for Graphical Models and Causal Inference

Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG

Description

These functions perform the last step in the PC algorithm: Transform an object of the class "pcAlgo" containing a skeleton and corresponding conditional independence information into a completed partially directed acyclic graph (CPDAG). The functions first determine the v-structures, and then apply the three orientation rules as described in Sprirtes et al (2000) and Meek (1995) to orient as many of the remaining edges as possible.

In the oracle version and when all assumptions hold, all three functions yield the same CPDAG. In the sample version, however, the resulting CPDAG may be invalid in the sense that one cannot extend it a DAG without additional unshielded colliders by orienting the undirecting edges. This can for example happen due to errors in the conditional indepedence tests or violations of the faithfulness assumption. The three functions deal with such conflicts in different ways, as described in Details.

Usage

udag2pdag       (gInput, verbose)
udag2pdagRelaxed(gInput, verbose, unfVect=NULL, solve.confl=FALSE,
  orientCollider = TRUE, rules = rep(TRUE, 3))
udag2pdagSpecial(gInput, verbose, n.max=100)

Arguments

gInput	"pcAlgo"-object containing skeleton and conditional indepedence information.
verbose	0: No output; 1: Details
unfVect	vector containing numbers that encode ambiguous triples (as returned by pc.cons.intern). This is needed in the conservative and majority rule PC algorithms.
solve.confl	if TRUE, the orientation of the v-structures and the orientation rules work with lists for candidate sets and allow bi-directed edges to resolve conflicting edge orientations. Note that therefore the resulting object is order-independent but might not be a PDAG because bi-directed edges can be present.
n.max	maximum number of tries for re-orienting doubly visited edges in udag2pdagSpecial.
orientCollider	if TRUE, collider are oriented.
rules	Array of length 3 containing TRUE or FALSE for each rule. TRUE in position i means that rule i (Ri) will be applied. By default, all rules are used.

Details

for udag2pdag:

If there are edges that are part of more than one v-structure (i.e., the edge b - c in the v-structures a -> b <- c and b -> c <- d), earlier edge orientations are simply overwritten by later ones. Thus, if a -> b <- c is considered first, the edge b - c is first oriented as b <- c and later overwritten by b -> c. The v-structures are considered in lexicographical ordering.

If the resulting graph is extendable to a DAG without additional v-structures, then the rules of Meek (1995) and Spirtes et al (2000) are applied to obtain the corresponding CPDAG. Otherwise, the edges are oriented randomly to obtain a DAG that fits on the skeleton, discarding all information about the v-structures. The resulting DAG is then transformed into its CPDAG. Note that the output of udag2pdag is random whenever the initial graph was not extendable.

Although the output of udag2pdag is always extendable, it is not necessarily a valid CPDAG in the sense that it describes a Markov equivalence class of DAGs. For example, two v-structures a -> b <- c and b -> c <- d (considered in this order) would yield the output a -> b -> c <- d. This is extendable to a DAG (it already is a DAG), but it does not describe a Markov equivalence class of DAGs, since the DAG a <- b -> c <- d describes the same conditional independencies.

for udag2pdagSpecial:

If the graph after orienting the v-structures as in udag2pdag is extendable to a DAG without additional v-structures, then the rules of Meek (1995) and Spirtes et al (2000) are applied to obtain the corresponding CPDAG. Otherwise, the algorithm tries at most n.max different random orderings of the v-structures (hence overwriting orientations in different orders), until it finds one that yields an extendable CPDAG. If this fails, the edges are oriented randomly to obtain a DAG that fits on the skeleton, discarding all information about the v-structures. The resulting DAG is then transformed into its CPDAG. Note that the output of udag2pdagSpecial is random whenever the initial graph was not extendable.

Although the output of udag2pdag is always extendable, it is not necessarily a valid CPDAG in the sense that it describes a Markov equivalence class of DAGs. For example, two v-structures a -> b <- c and b -> c <- d (considered in this order) would yield the output a -> b -> c <- d. This is extendable to a DAG (it already IS a DAG), but it does not describe a Markov equivalence class of DAGs, since the DAG a <- b -> c <- d describes the same conditional independencies.

for udag2pdagRelaxed:

This is the default version in the PC/RFCI/FCI algorithm. It does not test whether the output is extendable to a DAG without additional v-structures.

If unfVect = NULL (no ambiguous triples), the three orientation rules are applied to each eligible structure until no more edges can be oriented. Otherwise, unfVect contains the numbers of all ambiguous triples in the graph as determined by pc.cons.intern. Then the orientation rules take this information into account. For example, if a -> b - c and <a,b,c> is an unambigous triple and a non-v-structure, then rule 1 implies b -> c. On the other hand, if a -> b - c but <a,b,c> is an ambiguous triple, then the edge b - c is not oriented.

If solve.confl = FALSE, earlier edge orientations are overwritten by later ones as in udag2pdag and udag2pdagSpecial.

If solv.confl = TRUE, both the v-structures and the orientation rules work with lists for the candidate edges and allow bi-directed edges if there are conflicting orientations. For example, two v-structures a -> b <- c and b -> c <- d then yield a -> b <-> c <-d. This option can be used to get an order-independent version of the PC algorithm (see Colombo and Maathuis (2014)).

We denote bi-directed edges, for example between two variables i and j, in the adjacency matrix M of the graph as M[i,j]=2 and M[j,i]=2. Such edges should be interpreted as indications of conflicts in the algorithm, for example due to errors in the conditional independence tests or violations of the faithfulness assumption.

Value

for udag2pdag() and udag2pdagRelaxed():

oriented "pcAlgo"-object.

for udag2pdagSpecial:

a list with components

pcObj

An oriented "pcAlgo"-object.

evisit

Matrix counting the number of orientation attempts per edge

xtbl.orig

Logical indicating whether the original graph with v-structures is extendable.

xtbl

Logical indicating whether the final graph with v-structures is extendable

amat0

Adjacency matrix of original graph with v-structures (type amat.cpdag) .

amat1

Adjacency matrix of final graph with v-structures after changing the ordering in which the v-structures are considered (type amat.cpdag) .

status

Integer code with values

0:

Original try is extendable;

1:

Reorienting double edge visits helps;

2:

Original try is not extendable; reorienting double visits does not help; result is acyclic, has original v-structures, but perhaps additional v-structures.

counter

Number of orderings of the v-structures until success or n.max.

Author(s)

Markus Kalisch ([e-mail])

References

C. Meek (1995). Causal inference and causal explanation with background knowledge. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95), pp. 403-411. Morgan Kaufmann Publishers, Inc.

P. Spirtes, C. Glymour and R. Scheines (2000) Causation, Prediction, and Search, 2nd edition, The MIT Press.

J. Pearl (2000), Causality, Cambridge University Press.

D. Colombo and M.H. Maathuis (2014).Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15 3741-3782.

See Also

pc, pdag2dag, dag2cpdag, udag2pag, udag2apag, dag2pag.

Examples

## simulate data
set.seed(123)
p <- 10
myDAG <- randomDAG(p, prob = 0.2)
trueCPDAG <- dag2cpdag(myDAG)
n <- 1000
d.mat <- rmvDAG(n, myDAG, errDist = "normal")

## estimate skeleton
resU <- skeleton(suffStat = list(C = cor(d.mat), n = n),
                 indepTest = gaussCItest, ## (partial correlations)
                 alpha = 0.05, p=p)

## orient edges using three different methods
resD1 <- udag2pdagRelaxed(resU, verbose=0)
resD2 <- udag2pdagSpecial(resU, verbose=0, n.max=100)
resD3 <- udag2pdag       (resU, verbose=0)

pcalg documentation built on May 29, 2024, 5:24 a.m.