Searching for the Causal Structure of a Vector Autoregression*
介绍图论方法用于因果分析，推广了Swanson和Granger的工作，展示如何用图论方法选择结构向量自回归的因果顺序，并通过蒙特卡洛研究评估PC算法。
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.