科学研究中因果推断的方法、应用与展望--以个体健康研究为例

Methods,Applications and Prospects of Causal Inference in Scientific Research:A Study of Individual Health Research

以个体健康研究为例,探讨科学研究中因果推断的方法、应用与展望。因果推断方法作为研究个体健康影响机制的重要手段,有助于促进健康相关政策制定的科学化、合理化,为个体健康提供更为可靠的社会保障,对提高居民健康水平具有十分重要的社会意义。从个体健康研究中的常见问题、因果推断理论框架、实证研究中的因果推断三个方面对现有文献进行分析和评价。作为涵盖范围最广泛的研究领域,个体健康水平受到社会资本、收入、教育、保险、迁移、退休、工作等多类因素的影响,同时,实证研究中主要存在测量偏误、遗漏变量、互为因果、共同原因和选择偏差五类问题,解决这些问题常用的因果推断方法主要包括随机控制实验、倾向得分匹配、工具变量法、双重差分法、断点回归设计以及个体固定效应模型。研究阐述了这些因果推断模型的适用条件及优缺点,并对各类模型在个体健康实证研究中的应用作了简要的总结和分析。研究还有助于学者在个体健康研究领域中选择合适的因果推断方法,或进一步综合应用已有的方法。随着大数据技术的发展和对因果推断方法的深入了解,未来应加强机器学习和因果推断方法的结合,丰富已有的因果推断工具,保障研究结果的稳健性。

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.