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JMLR
2010

Learning Causal Structure from Overlapping Variable Sets

11 years 2 months ago
Learning Causal Structure from Overlapping Variable Sets
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges corresponding to all possible pairwise causal relations between two variables, named Pairwise Causal Graph (PCG). Examples of interesting inferences include the induction of the absence or presence of some causal relation between two variables never measured together. cSAT+ converts the problem to a series of SAT problems, obtaining leverage from the efficiency of state-ofthe-art solvers. In our empirical evaluation, it is shown to outperform ION, the first algorithm solving a similar but more general problem, by two orders of magnitude.
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G. Tollis
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