Patent Document ID: 8655821
Application ID: 12700689

Base Claim:
1. A computer-implemented method termed GLL-PC for finding a set of direct causes and direct effects of the response/target variable or for finding a Markov blanket of the response/target variable for local causal discovery or feature selection, said method comprising the following steps all of which are performed on a computer: 1) find an approximation of the set of direct causes and direct effects of response variable T using the following steps: a) initialize a set of candidates to be a subset of all variables minus the response variable T; b) initialize a priority queue of variables to be examined for inclusion in the candidates set from the remaining variables; c) apply a user-provided inclusion heuristic function to prioritize variables in the priority queue for inclusion in the candidates set; d) remove non-eligible variables from the priority queue; e) insert in the candidates set the highest-priority variable(s) in the priority queue and remove them from the priority queue; f) apply a user-provided elimination strategy to remove variables from the candidates set; g) iterate among the inclusion, prioritization and elimination steps c, d, e and f according to a user-provided interleaving strategy until a termination criterion is met; variables may be re-ranked after each update of the candidate set, or the original ranking may be used throughout the method's operation; 2) store the candidate set resulting from step (1) in PC T ; 3) for every variable X i belonging in PC T , apply step (1) designating X i as the response variable; 4) for every variable X i belonging in PC T , if the response variable T is not in the output of step (3) for the response variable X i , then eliminate X i from PC T ; and 5) output the set PC T .

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Claim 2:
2. The computer-implemented method of claim 1 in which: a) step (1.c) applies an inclusion heuristic function that satisfies the following requirements: (i) all variables that are direct causes or direct effects of the response variable T are eligible for inclusion in the candidate set, and each such variable is assigned a non-zero value by the heuristic function; (ii) variables with zero values are discarded and never considered again; b) step (1.f) applies an elimination strategy that satisfies the following requirements: all and only variables that are probabilistically independent of the response variable T given any subset of the candidate set are discarded and never considered again (whether they are inside or outside the candidate set); and c) step (1.g) iterates inclusion and elimination any number of times provided that iterating stops when the following criterion is satisfied: at termination no variable outside the candidates is eligible for inclusion and no variable in the candidates set can be removed at termination.