R Under development (unstable) (2024-02-11 r85891 ucrt) -- "Unsuffered Consequences"
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> library(pcalg)
> (doExtras <- pcalg:::doExtras())
[1] FALSE
> if (doExtras) {
+ ##################################################
+ ## 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')
+ fci.conf <- pag2conf(fci.pag@amat)
+ print(fci.conf)
+ 
+ true.conf <- rbind(c(-1, 0, 0, 0,-1,-1,-1,-1),
+                    c( 0,-1, 0,-1, 0,-1,-1,-1),
+                    c( 0, 0,-1,-1, 0, 0,-1,-1),
+                    c( 0,-1,-1,-1,-1,-1, 0,-1),
+                    c(-1, 0, 0,-1,-1, 0,-1, 0),
+                    c(-1,-1, 0,-1, 0,-1,-1,-1),
+                    c(-1,-1,-1, 0,-1,-1,-1, 0),
+                    c(-1,-1,-1,-1, 0,-1, 0,-1))
+ 
+ rownames(true.conf)<-V
+ colnames(true.conf)<-V
+ 
+ stopifnot(identical(true.conf,fci.conf))
+ }
> 
> proc.time()
   user  system elapsed 
   1.29    0.21    1.50