Shrout P E
Department of Psychology, New York University, NY 10003, USA.
Soc Psychiatry Psychiatr Epidemiol. 1998 Aug;33(8):400-4. doi: 10.1007/s001270050072.
This paper reviews the logic of causal inference from epidemiological data. I maintain that the clearest causal statements can be made when the philosophical causal principles of association, direction and isolation are upheld in epidemiological research. After reviewing the argument by Holland that only experimental manipulation affords clear causal claims, I examine the utility of structural equation models and longitudinal methods for making causal claims from non-experimental data. This examination leads to the conclusion that mental health epidemiologists should begin to incorporate intervention trials into the last phases of their research programmes when they want to make strong causal claims.
本文回顾了从流行病学数据进行因果推断的逻辑。我认为，当流行病学研究坚持关联、方向性和孤立性这些哲学因果原则时，就能做出最清晰的因果陈述。在回顾了霍兰德的观点（即只有实验性操纵才能提供明确的因果主张）之后，我考察了结构方程模型和纵向研究方法在从非实验数据得出因果主张方面的效用。这一考察得出的结论是，心理健康流行病学家若想做出有力的因果主张，就应在其研究项目的最后阶段开始纳入干预试验。
