CCPR Seminar

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.

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.

Diversity, Immigration, and Public Opinion in the U.S.

Abstract: What factors animate public opinion towards immigrants? A substantial literature has tested the impact of individual objective traits like education and job market status on immigration attitudes. In addition, researchers have explored the role of subjective factors like immigrants’ perceived impact on society. However, prior quantitative research has generally overlooked a key aspect: natives’ impressions of who immigrants are. Immigrants in the U.S. are increasingly diverse and evidence suggests that natives prefer certain types of immigrants. Yet, survey questions gauging immigration attitudes often refer to “immigrants” as if they were a single, homogenous group, which makes it hard to interpret survey takers’ answers. To fill this gap, we explore heterogeneity in subjective perceptions of immigrants and assess their attitudinal impacts. We systematically uncover these perceptions by using a Latent Class Analysis approach on a new set of survey items we developed. We find the presence of four different immigrant “archetypes” or multidimensional constellations of immigrant traits. These archetypes are shared across regions, social classes, and partisan lines and more powerfully predict immigration attitudes than typical independent variables used in extant research. Last, we discuss the theoretical and methodological implications of our findings.