Causal inference is an important method to study the mechanism of individual health effects, which helps to promote the scientific and rationalization of health-related policies, provides more reliable social security for individual health, and has very important social significance for improving the health of residents. This article analyzes and evaluates the existing literature from three aspects: common problems in individual health research, theoretical framework of causal inference, and causal inference in empirical research. As the most extensive research field, individual health level is affected by social capital, income, education, insurance, migration, retirement, work and other factors. Meanwhile, there are mainly five types of problems such as measurement error, omitted variable, reverse causation, common causes, and selection bias. The common causal inference methods for these problems include randomized controlled trials, propensity score methods, instrumental variables, double difference methods, regression discontinuity design, and individual fixed effects model. In this article, the applicable conditions, advantages and disadvantages of these causal inference models are expounded; meanwhile, the application of various models in empirical research on individual health is briefly summarized and analyzed. The study also helps scholars to choose appropriate causal inference methods in the field of individual health research, or further comprehensively apply existing methods. With the development of big data technology and an in-depth understanding of causal inference methods, in future research, the combination of machine learning and causal inference methods should be strengthened to enrich the existing causal inference tools and ensure the robustness of research results.
