Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD, 21205, USA.
Prev Sci. 2019 Apr;20(3):452-456. doi: 10.1007/s11121-018-0971-9.
With innovation in causal inference methods and a rise in non-experimental data availability, a growing number of prevention researchers and advocates are thinking about causal inference. In this commentary, we discuss the current state of science as it relates to causal inference in prevention research, and reflect on key assumptions of these methods. We review challenges associated with the use of causal inference methodology, as well as considerations for hoping to integrate causal inference methods into their research. In short, this commentary addresses the key concepts of causal inference and suggests a greater emphasis on thoughtfully designed studies (to avoid the need for strong and potentially untestable assumptions) combined with analyses of sensitivity to those assumptions.
随着因果推理方法的创新和非实验数据可用性的提高，越来越多的预防研究人员和倡导者开始思考因果推理。在这篇评论中，我们讨论了与预防研究中的因果推理相关的科学现状，并反思了这些方法的关键假设。我们回顾了使用因果推理方法所面临的挑战，以及将因果推理方法纳入其研究的考虑因素。简而言之，本评论探讨了因果推理的关键概念，并建议更加强调精心设计的研究（以避免对强假设和潜在未经检验的假设的需要），同时结合对这些假设的敏感性分析。
