a, b, c: (integer) positions in adjacency matrix for nodes a, b, and c, respectively.
nbrsA, nbrsC: (integer) position in adjacency matrix for neighbors of a and c, respectively.
sepsetA: vector containing Sepset(a,c).
sepsetC: vector containing Sepset(c,a).
suffStat: a list of sufficient statistics for independent tests; see, e.g., pc.
indepTest: a function for the independence test, see, e.g., pc.
alpha: significance level of test.
version.unf: (integer) vector of length two:
version.unf[1]:: 1 - check for all separating subsets of nbrsA and nbrsC if b is in that set,
2 - it also checks if there at all exists any sepset which is a subset of the neighbours (there might be none, although `b`
is in the sepset, which indicates an ambiguous situation);
version.unf[2]:: 1 - do not consider the initial sepsets sepsetA and sepsetC (same as Tetrad),
2 - consider if `b` is in `sepsetA` or `sepsetC`.
maj.rule: logical indicating that the following majority rule is applied: if b is in less than 50% of the checked sepsets, we say that b is in no sepset. If b is in more than 50% of the checked sepsets, we say that
b is in all sepsets. If b is in exactly 50% of the checked sepsets, the triple is considered ambiguous .
verbose: Logical asking for detailed output of intermediate steps.
Details
This function is used in the conservative versions of structure learning algorithms.
Returns
decision: Decision on possibly ambiguous triple, an integer code,
1: b is in NO sepset (make v-structure);
2: b is in ALL sepsets (make no v-structure);
3: b is in SOME but not all sepsets (ambiguous triple)
vers: Version (1 or 2) of the ambiguous triple (1=normal ambiguous triple that is b is in some sepsets; 2=triple coming from version.unf[1]==2, that is, a and c are indep given the initial sepset but there doesn't exist a subset of the neighbours that d-separates them.)
sepsetA: Updated version of sepsetA
sepsetC: Updated version of sepsetC
References
D. Colombo and M.H. Maathuis (2014).Order-independent constraint-based causal structure learning. Journal of Machine Learning Research