Granger causality and Bayesian network inference applied on insufficient number of data points for non-linear model. The grey edges in the inferred network structures indicate undetected causalities in our defined toy model. For each sample size n, we simulated a data set of 100 realizations of n time points. The Bayesian network structure represents a model average from these 100 realizations. High-confidence arcs, appearing in at least 95% of the networks are shown. The Granger causality inferred the structure according to the 95% confidence interval constructed by using the bootstrap method. (A) The sample size is 300. (B) The sample size is 150. (C) The sample size is 50.