My goal is to build an independent research program in the development of causal inference methods for investigating environmental causes of childhood cancer. This K01 will enable me to conduct the focused, intensive research that will lay the groundwork for that program and to acquire the environmental, biological, and epidemiological training needed to maximize the rigor and impact of my work. Research: We propose to develop new causal machine learning (ML) methods that enable rigorous analysis of environmental natural experiments (NE) for estimation of the causal effects of environmental exposures on childhood cancer. Classical approaches to studying relationships between environmental exposures and childhood cancer are plagued with challenges and are yielding inconsistent findings. We contend that the recent proliferation of local environmental regulatory programs has created ample relevant NEs, which provide a powerful alternative approach to study these relationships. However, existing methods for NE analysis are poorly-suited for environmental health contexts. In particular, existing methods fail in the presence of rare outcomes like childhood cancer (Aim 1), and they are not able to provide insight into the timing at which children are most susceptible to any adverse exposure effects (Aim 2). We propose causal ML methods that overcome these challenges and apply them to a NE to study the effects of traffic-related air toxics on childhood leukemia. We also provide open source software implementing these methods (Aim 3). Career Development and Training: Given my extensive prior training and experience in statistics and data science, the primary aim of the training funded by this award will be the acquisition of subject-matter proficiency, which will provide me with the insights needed to create more effective and impactful environmental health methods. Specifically, I will pursue knowledge in the biology and epidemiology of childhood cancer and in environmental health and exposure biology. The training will be achieved through a combination of (1) hands-on collaborative research as described above; (2) intensive cross-disciplinary mentorship, with mentors specializing in environmental health, pediatric oncology, cancer biology and epidemiology, and statistics; (3) carefully-selected coursework in the Departments of Epidemiology, Environmental Health, and Cell Biology at Harvard; and (4) relevant conferences, workshops, and seminars. I will place special emphasis on establishing a network of expert collaborators in all my areas of training. Environment: The Harvard Medical Campus is home to the top research teams worldwide in both childhood cancer and environmental health. Due to Harvard?s position at the forefront of scientific discovery in these fields, its unparalleled resources, its vibrant intellectual atmosphere, and its promotion of collaborative science that integrates knowledge across disciplines, it provides an ideal environment in which to train on these topics.
Decades of research on the relationship between environmental exposures and childhood cancer, relying on classic epidemiologic study designs, has produced a largely inconclusive body of literature, and has generated little robust, actionable evidence to protect children?s health. We propose an alternative methodological approach to this problem, which relies on environmental natural experiments to overcome many of the challenges presented by classic approaches. We develop causal machine learning methods tailored to this setting, which provide a promising new avenue for obtaining robust evidence about environmental causes of childhood cancer, and we apply the methods to study the effects of exposure to traffic-related air toxics on childhood leukemia.
