Measurement invariance refers to the equivalence of measurement instrument in different groups. Research on social science often involves comparing different groups, such as whether the relationship between two variables is the same in male and female groups. Measurement invariance is a prerequisite of these studies because if the measurement tools are not equivalent, we cannot distinguish the difference between the degree of measurement tools and the empirical results. The causal model proposed by Michael Siegrist is one of the baseline models for studying public acceptance of genetically modified food, but only a few studies have tested the invariance of the causal model. Thus, it is difficult for researchers to judge the reliability of some conclusions about group comparison, such as whether the risk perception of men is lower than that of women. In this study, we use sample data about China (N = 1091) to test the invariance of the causal model among groups with different genders and knowledge levels. The test results show that the model has full invariance across gender, and only factor loading invariance has no measurement error invariance across knowledge levels. The results of this study show that the conclusion about group comparison on gender in previous studies is credible, but the reliability of the measurement of the differences between knowledge level groups needs to improve before meaningful comparison can be made.