1. A computer-implemented method for parallel or distributed predictive, causal and feature selection analysis of Big Data comprising the following steps: a) initializing dataset D, variables V, response variable T, and parameter max-k (maximum size of the conditioning set) with a non-negative integer, as defined by a user; b) initializing M with an empty set; c) initializing E with all variables excluding T; d) initializing the conditioning subset C[1] with an empty set; e) repeating the following steps e(1)-e(7) until the exit condition is met; (1) iterating in parallel or distributed fashion over variables X in E and conditioning subsets in C; (a) if T becomes statistically independent of X given some conditioning subset C[i], removing X from E; (2) if max-k is equal to zero, assigning E to M, outputting M, and terminating; (3) if E is empty, exiting from the iterative loop and proceeding to step f below; (4) selecting a variable Y that maximizes association with T over variables in E; (5) assigning to C all subsets of M of size up to max-k−1, union with the variable Y; 6) adding Y to M; (7) removing Y from E; f) iterating in parallel or distributed fashion over variables X in M; (1) assigning to C all non-empty subsets of M excluding X of size up to max-k; (a) iterating in parallel or distributed fashion over conditioning subsets in C; (i) if T becomes statistically independent of X given some conditioning subset C[i], removing X from M; and g) outputting M.