Phillips Carl V, Goodman Karen J
University of Alberta School of Public Health, Edmonton, Canada.
Emerg Themes Epidemiol. 2006 May 26;3:5. doi: 10.1186/1742-7622-3-5.
Two persistent myths in epidemiology are that we can use a list of "causal criteria" to provide an algorithmic approach to inferring causation and that a modern "counterfactual model" can assist in the same endeavor. We argue that these are neither criteria nor a model, but that lists of causal considerations and formalizations of the counterfactual definition of causation are nevertheless useful tools for promoting scientific thinking. They set us on the path to the common sense of scientific inquiry, including testing hypotheses (really putting them to a test, not just calculating simplistic statistics), responding to the Duhem-Quine problem, and avoiding many common errors. Austin Bradford Hill's famous considerations are thus both over-interpreted by those who would use them as criteria and under-appreciated by those who dismiss them as flawed. Similarly, formalizations of counterfactuals are under-appreciated as lessons in basic scientific thinking. The need for lessons in scientific common sense is great in epidemiology, which is taught largely as an engineering discipline and practiced largely as technical tasks, making attention to core principles of scientific inquiry woefully rare.
一是我们可以使用“因果标准”列表来提供一种推断因果关系的算法方法；二是现代的“反事实模型”可以在这一过程中提供帮助。我们认为，这些既不是标准也不是模型，但因果考量列表和因果关系反事实定义的形式化仍是促进科学思维的有用工具。它们让我们走上科学探究常识的道路，包括检验假设（真正对其进行检验，而不仅仅是计算简单的统计数据）、应对迪昂 - 奎因问题以及避免许多常见错误。因此，奥斯汀·布拉德福德·希尔的著名考量，一方面被那些将其用作标准的人过度解读，另一方面又被那些认为其有缺陷而不予理会的人低估。同样，反事实的形式化作为基础科学思维的经验教训也未得到充分重视。在流行病学领域，对科学常识经验教训的需求非常大，因为流行病学在很大程度上是作为一门工程学科来教授，并且在很大程度上是作为技术任务来实践的，这使得对科学探究核心原则的关注少之又少。
