Preprints and Papers
Below is a list of the papers and preprints that have been written by members of the Causal Lab.
Complex Systems Models for Causal Inference in Social Epidemiology
Hiba N Kouser, Ruby Barnard-Mayers, Eleanor Murray
Journal of Epidemiology and Community Health
Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.
Ruby Barnard-Mayers, Ellen Childs, Laura Corlin, Ellen Caniglia, Matthew P Fox, John P. Donnelly, Eleanor J Murray
Medrxiv
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, a majority of participants reported being comfortable with using causal graphs and reported using them ‘sometimes’, ‘often’, or ‘always’ in their research. Almost three quarters of respondents had received formal training on causal graphs (typically causal directed acyclic graphs). Having received training appeared to improve comprehension of the underlying assumptions of 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. Of the participants who did not use DAGs, many expressed that trainings, either in-person or online, would be useful resources to help them use causal graphs more often in their research. Conclusion 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.
Ruby Barnard-Mayers, Hiba Kouser, Jamie A. Cohen, Katherine Tarriopoulos, Ellen C. Caniglia, Anna-Barbara Moscicki, Nicole G. Campos, Michelle R. Caunca, George R. Seage III, Eleanor Murray
OSF Preprints
Background: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. Recent findings have suggested that the human papillomavirus (HPV) vaccine is less effective in protection against HPV associated disease in a population of girls living with HIV. Development: In order to understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. Application: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions prior to data analysis and exemplify the process for designing an etiologic study. Conclusion: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research prior to undergoing data collection and/or analysis.
Kailin Xu, Eleanor J Murray
BU Undergrad Research Symposium Fall 2021
Introduction: Human papillomavirus (HPV) is one of the most common sexually transmitted diseases and infection is related to kinds of cancers including cervical, oropharyngeal, and anogenital (vaginal vulvar, anal, and penile) cancers. Two high-risk HPV genotypes, HPV-16 and -18, account for the majority of HPV-related cancer burden in the U.S. Currently, there are three types of HPV vaccines available in the United States: bivalent, quadrivalent, and nonavalent vaccines. The HPV vaccine is most recommended for adolescents aged 9 to 13 and encourages everyone before 26 years old. HPV vaccination is given as a series of two to three doses, and two kinds of screening tests help to detect lesions in an early stage (Pap smear and HPV DNA test). Recommendations for women in different age group varies. Systematic research was conducted and both qualitative and quantitative evidence were mapped to understand HPV vaccine and screening accessibility in the U.S. healthcare system.