Causal inference (CI) is one of the popular performance diagnosis methods, which infers the anomaly propagation from the observed data for locating the root causes. Although some specific CI methods have been employed, the overall performance of this class of methods on microservice performance diagnosis is not well understood. To this end, we select six representative CI methods from three categories and evaluate their performance against the challenges of microservice operations, including the large-scale observable data, heterogeneous anomaly symptoms, and a wide range of root causes. Our experimental results show that 1) CI techniques must be integrated with anomaly detection or anomaly scores to differentiate the causality in normal and abnormal data; 2) CI techniques are more robust to false positives in anomaly detection than knowledge-based non-CI method; 3) To get the fine-grained root causes, an effective way with CI techniques is to identify the faulty service first and infer the detailed explanation of the service abnormality. Overall, this work broadens the understanding of how CI methods perform on microservice performance diagnosis and provides recommendations for an efficient application of CI methods.