Assessing Knowledge, Attitudes, and Practices towards Causal Directed Acyclic Graphs among Epidemiologists and Medical Researchers: a qualitative research project


Background: Estimating the strength of causal effects is an important component of epidemiologic research, and causal graphs provide a key tool for optimizing the validity of these effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, including directed acyclic graphs, to assess and describe causal assumptions, and translate these assumptions into appropriate statistical analysis plans, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. Objective We sought to understand this gap 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. Methods We conducted an anonymous survey of self-identified epidemiology and health researchers via Twitter and via the Society of Epidemiologic Research membership listserv. The survey was conducted using Qualtrics and asked a series of multiple choice and open-ended questions about causal graphs. Results In total, 439 responses were collected. Overall, 62% reported being comfortable with using causal graphs, and 60% reported using them 9sometimes9, 9often9, or 9always9 in their research. About 70% of respondents had received formal training on causal graphs (typically causal directed acyclic graphs). Having received any training appeared to improve comprehension of the underlying assumptions of causal graphs. Forty percent of respondents who did not use causal graphs reported lack of knowledge …

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