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• W3121795838 abstract "We provide an accessible introduction to graph-theoretic methods for causal analysis. Building on the work of Swanson and Granger (Journal of the American Statistical Association, Vol. 92, pp. 357–367, 1997), and 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 class. Our findings suggest that graph-theoretic methods may prove to be a useful tool in the analysis of SVARs." @default.
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• W3121795838 date "2003-12-01" @default.
• W3121795838 modified "2023-10-03" @default.
• W3121795838 title "Searching for the Causal Structure of a Vector Autoregression*" @default.
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• W3121795838 doi "https://doi.org/10.1046/j.0305-9049.2003.00087.x" @default.
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