7. A computer-implemented method termed GLL-MB 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 ; 5) for every variable Y that belongs to PC T , run steps (1)-(4) to obtain the sets PC Y ; 6) initialize the set of candidate Markov blanket variables MB T of the response variable T with PC T ; 7) initialize the set of potential spouses that are not direct causes and effects of T (S) with the union of variables in PC Y (over all Y belonging to PC T ) excluding the response variable T and the set PC T ; 8) eliminate non-spouses of T from S by applying the following steps for every variable X in S: a) retrieve or obtain a subset Z that makes X probabilistically independent of the response variable T variable given Z; b) for every variable Y that belongs to PC T such that X belongs to PC Y , implying that Y is a potential common child of T and X: If the response variable T is not probabilistically independent from X given the union of Z and Y then conclude that X is a spouse of the response variable T and insert X into the set of candidate Markov blanket members of the response variable T (MB T ); 9) optionally: eliminate from MB T variables that are predictively redundant for the response variable T using one of many established backward wrapper approaches; and 10) output the set MB T .
7. A computer-implemented method termed GLL-MB 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 ; 5) for every variable Y that belongs to PC T , run steps (1)-(4) to obtain the sets PC Y ; 6) initialize the set of candidate Markov blanket variables MB T of the response variable T with PC T ; 7) initialize the set of potential spouses that are not direct causes and effects of T (S) with the union of variables in PC Y (over all Y belonging to PC T ) excluding the response variable T and the set PC T ; 8) eliminate non-spouses of T from S by applying the following steps for every variable X in S: a) retrieve or obtain a subset Z that makes X probabilistically independent of the response variable T variable given Z; b) for every variable Y that belongs to PC T such that X belongs to PC Y , implying that Y is a potential common child of T and X: If the response variable T is not probabilistically independent from X given the union of Z and Y then conclude that X is a spouse of the response variable T and insert X into the set of candidate Markov blanket members of the response variable T (MB T ); 9) optionally: eliminate from MB T variables that are predictively redundant for the response variable T using one of many established backward wrapper approaches; and 10) output the set MB T . 9. The computer-implemented method of claim 7 in which step (1.c) applies the inclusion heuristic function implemented using one of the following methods: a) based on random search over values of standard heuristics; b) based on observed probability of a variable to remain in the candidate set after conditioning on many subsets of variables; c) based on known structure of the data generating process and/or distributional characteristics.