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Data-driven fault dignosis methods have been widely studied and applied in recent years, but these methods are mainly focus on the fault detection, and how to determine the fault source has not been fully solved. This paper presents a causal analysis fault location method(PCA-PRF) based on principal component analysis(PCA) and random forest regression(RFR). In this method, the variables in the off-line fault data segment and the corresponding statistics are treated as the input and the output, respectively, and then the causal coefficient from process variables to statistics can get according to the importance measurement of variables based on the random forest regression model. The variable with larger importance measurement is considered to be more likely fault. A numerical case and the Tenessee Eastman Process(TEP) simulation experiment are employed to demonstrate the application of the proposed method, shows the effectiveness of this proposed method.)