We provide an accessible introduction to graph-theoretic methods for causal analysis. generalizing to a larger class of models, we show how to apply graph-theoretic methods to selecting the causal order for a structural vector autoregression (SVAR). We evaluate the PC (causal search) algorithm in a Monte Carlo study. The PC algorithm uses tests of conditional independence to select among the possible causal orders -or at least to reduce the admissible causal orders to a narrow equivalence

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... Our findings suggest that graph-theoretic methods may prove to be a useful tool in the analysis of SVARs. I. The problem of causal order Drawing on recent work on the graph-theoretic analysis of causality, we propose and evaluate a statistical procedure for identifying the contemporaneous causal order of a structural vector autoregression. *We thank Marcus Cuda for his help with programming and computational design, Derek Stimel and Ryan Brady for able research assistance, and to