Causal graphs provide a key tool for optimizing the validity of causal effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. We sought to understand why researchers do or do not regularly use DAGs by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. We used Twitter and the Society for Epidemiologic Research to disseminate the survey. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them ‘sometimes’, ‘often’, or ‘always’ in their research. Having received training appeared to improve comprehension of the assumptions displayed in causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.

因果图提供了优化因果效应估计有效性的关键工具。尽管存在大量关于使用因果图的数学理论的文献，但帮助应用研究人员了解如何在其研究项目中最好地开发和使用因果图的文献却很少。我们通过调查执业流行病学家和医学研究人员对在应用流行病学和健康研究中使用因果图的知识、兴趣程度、态度和实践，试图了解研究人员经常或不经常使用 DAG 的原因。我们使用 Twitter 和流行病学研究协会来传播这项调查。总体而言，大多数参与者表示对使用因果图感到满意，并表示在他们的研究中“有时”、“经常”或“总是”使用它们。接受培训似乎可以提高对因果图中显示的假设的理解。许多未使用因果图的受访者表示，缺乏知识是在研究中使用 DAG 的障碍。流行病学家和医学研究人员对因果图很感兴趣，但对其理解存在一些障碍。需要额外的培训和更明确的指导。此外，需要发展关于效果测量修改和因果图交互可视化的方法。