pag2conf: Reads off identifiable unconfounded node pairs from a...

In pcalg: Methods for Graphical Models and Causal Inference

Reads off identifiable unconfounded node pairs from a directed PAG

Description

Constructs a matrix which contains identifiably unconfounded node pairs in the Markov equivalence class represented by a directed partial ancestral graph.

Usage

pag2conf(P)

Arguments

P	Adjacency matrix of type amat.pag, which should encode a directed PAG (i.e., it should not contain any undirected edges of the form x --- y or any circle-tail edges of the form x o-- y).

Details

We say that nodes i and j are confounded in a directed mixed graph (DMG) iff there exists a bidirected edge i<->j in that graph. If the directed mixed graph has a causal interpretation (for example, if it is the graph of a simple SCM) then the presence of a bidirected edge coincides (generically) with the presence of a confounder, i.e., a latent common cause (relative to the variables in the graph).

This function implements the sufficient condition (Proposition 6) in Mooij and Claassen (2020) for concluding whether two nodes are unconfounded in all directed mixed graphs in the Markov equivalence class represented by the directed PAG P. It applies to both the acyclic case as well as the cyclic (simple SCM) case, assuming the d-separation resp. \sigma-separation Markov property.

The output is a (symmetric) matrix containing for each ordered pair of nodes whether the two nodes are identifiably unconfounded.

It is not known whether these sufficient conditions for identifiability are complete. Hence, zero entries in the result indicate that the sufficient condition gives no conclusion, rather than that the Markov equivalence class represented by the directed PAG necessarily contains DMGs where a bidirected edge is present.

P should be an adjacency matrix of type amat.pag that contains no undirected and circle-tail edges.

Value

Matrix A, where entry A[i,j] equals

-1

if nodes i and j are identifiably unconfounded,

0

in case it is unknown whether nodes i and j are confounded or not.

Author(s)

Joris Mooij.

References

J. M. Mooij and T. Claassen (2020). Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. In Proc. of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20), 1159-1168.

Examples

##################################################
## Mooij et al. (2020), Fig. 43(a), p. 97
##################################################

# Encode ADMG as adjacency matrix
p <- 8 # total number of variables
V <- c("Ca","Cb","Cc","X0","X1","X2","X3","X4") # 3 context variables, 5 system variables
# amat[i,j] = 0 iff no edge btw i,j
# amat[i,j] = 1 iff i *-o j
# amat[i,j] = 2 iff i *-> j
# amat[i,j] = 3 iff i *-- j
amat <- rbind(c(0,2,2,2,0,0,0,0),
              c(2,0,2,0,2,0,0,0),
              c(2,2,0,0,2,2,0,0),
              c(3,0,0,0,0,0,2,0),
              c(0,3,3,0,0,3,0,2),
              c(0,0,3,0,2,0,0,0),
              c(0,0,0,3,0,0,0,2),
              c(0,0,0,0,2,0,3,0))
rownames(amat)<-V
colnames(amat)<-V

# Make use of d-separation oracle as "independence test"
indepTest <- dsepAMTest
suffStat<-list(g=amat,verbose=FALSE)


# Derive PAG that represents the Markov equivalence class of the ADMG with the FCI algorithm
# (assuming no selection bias)
fci.pag <- fci(suffStat,indepTest,alpha = 0.5,labels = V,verbose=TRUE,selectionBias=FALSE)

# Read off causal features from the FCI PAG
cat('Identified absence (-1) and presence (+1) of pairwise latent confounding from FCI PAG:\n')
print(pag2conf(fci.pag@amat))

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