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We consider the Granger causal structure learning problem from time seriesdata. Granger causal algorithms predict a 'Granger causal effect' between twovariables by testing if prediction error of one decreases significantly in theabsence of the other variable among the predictor covariates. Almost allexisting Granger causal algorithms condition on a large number of variables(all but two variables) to test for effects between a pair of variables. Wepropose a new structure learning algorithm called MMPC-p inspired by the wellknown MMHC algorithm for non-time series data. We show that under someassumptions, the algorithm provides false discovery rate control. The algorithmis sound and complete when given access to perfect directed information testingoracles. We also outline a novel tester for the linear Gaussian case. We showthrough our extensive experiments that the MMPC-p algorithm scales to largerproblems and has improved statistical power compared to existing state of theart for large sparse graphs. We also apply our algorithm on a globaldevelopment dataset and validate our findings with subject matter experts.

Structure Learning from Time Series with False Discovery Control