Formation of study population for causal inference

Epidemiological analysis describes and compares the characteristics of a certain number of people to make causal inferences. The formation of the study population is always the first step. In this paper, we first define the concepts of cross-sections at both individual level and population level and introduce the three assumptions needed in the measurements in observational studies, i. e. the true values of the attributes are stable with time, the attribute variables are independent and the individuals are independent during the measuring process. We also determine that the causal inference research should be unified based on the time of the occurrence or beginning of a postulated cause, or exposure, should be in. Then, based on the dual roles of the population cross-section with causal thinking, we propose that research designs can be classified into two types with different characteristics: history reconstruction research and future exploration research. Finally, we briefly analyze the research design framework and the relationship between estimated effects and different designs. The discussion of the formation of a study population from the perspective of causal thinking can make a foundation for the classification of causal inference research design with appropriate effect parameters, which needs to be further studied.