Learning Causal Structure from Overlapping Variable Sets
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:860-867, 2010.
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variables 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-of-the-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.