## Dseptor## A Core-Guided Approach to Learning Optimal Causal Graphsis developed by the Constraint Reasoning and Optimization Group at the Department of Computer Science, University of Helsinki, and implemented by Antti Hyttinen and Paul Saikko on top of the LMHS MaxSAT solver. The main reference for dseptor is [1]. An online appendix to [1], including formal proofs and further details, is available here. ## UsageUSAGE: ./dseptor <file> [options] COMMAND LINE ARGUMENTS: <file> : Input filename in ... format. COMMAND LINE OPTIONS: -h : Display this help message. -v : Display the version of the program. -o <out> : Output clauses to file <out> in wcnf format and exit. ## Input formatThe input is a text file, in which each line corresponds to an independence test result. Each line has the form: i w x y c j where i 0 if the result is dependence or 1 if the result is independence w weight, a positive integer representing the reliability x the positive index of the first node (starting from 1) y the positive index of the second node c integer presentation of the conditioning set bit vector 0 = {}, 1 = {1}, 2= {2}, 3 = {1,2}, 4 = {3}, 5 = {1,3}, ... j integer presentation of the intervention set bit vector ## Downloads## dseptorThe program will eventually be made available here. ## BenchmarksBenchmarks used in [1] are available here. ## References
[1] |