Race, Culture and Health: Conceptual and Methodological Innovations

Abstract: Building a culture of health and achieving health equity requires that we engage cultural processes in a more meaningful way. Cultural processes and systems are commonly referenced in health inequity scholarship but empirical research generally lags behind this conceptual emphasis. I argue that employing a transdisciplinary approach to examining intersections of culture, structure and racism is a valuable analytical tool for understanding the production of social and racial inequities in health. In this talk, I’ll discuss conceptual work advancing the concept of “cultural racism” in relation to racial inequities in health and will also provide an overview of related empirical projects: 1) a laboratory experiment examining the effects of media-based racism on physiological, psychological and behavioral stress responses, 2) a data science project exploring ways to assess chronic exposure to media-based racism and possible links to population health and 3) the use of virtual reality to promote structural competence regarding the structural and cultural roots of racism. In lieu of a deep dive on a single project or paper, the presentation seeks to support a rich conversation about the need for conceptual and methodological innovation in service of better understanding and addressing racial inequities in health.

Machine Learning for Causal Inference

Abstract: Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible. In this talk, I'll discuss how these machine learning tools can be rigorously integrated into observational study analyses, and how they interact with classical ideas around randomization, semiparametric modeling, double robustness, etc. When deployed carefully, machine learning enables us to develop statistical estimators that reflect the study design more closely than basic linear regression based methods.