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Granger causality(GC)analysis has been widely used to investigate information flow through causal interactions.We address one of the central questions in GC analysis,that is,the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis.We demonstrate that different sampling rate may potentially yield completely opposite inferences.This inference hazard is present for both linear and nonlinear processes.In addition,GC analysis may completely fail to capture the causal relations for typical nonlinear time series.We present strategies to overcome those inference artifacts in order to obtain a reliable GC result and demonstrate that one should be very careful when applying GC analysis for nonlinear dynamical processes.