Workshop in Biostatistics

Medical School Office Building (MSOB)
Rm x303

DATE: November 9, 2017
TIME: 1:30 - 2:50 pm
TITLE: Machine learning approaches to understanding heterogenous effects of social policy on health
David Rehkopf
Assistant Professor of Medicine
(Primary Care and Population Health) and, by courtesy, of Health Research and Policy (Epidemiology), Stanford



The examination of the effects of social policies on health has understandably focused on the overall population average treatment effects. However, within this population average, there may be substantial differences in the effects of the policy, with the potential for a policy increasing or decreasing health inequalities depending on the population groups that do and do not benefit. Traditionally, examination of the heterogeneity of treatment effects has proceeded by priors from the literature, and due to power issues generally has examined only a few potential factors leading to heterogeneous effects. At the same time, there have been considerable advances in machine learning – including algorithms that scan over a large number of covariates to establish models of covariates that best explain a specified outcome, penalizing for degrees of freedom. Our analysis uses this general approach to examine potential heterogeneity of treatment effects of the largest anti-poverty policy in the United States, the Earned Income Tax Credit. We examine the changes in the generosity of the policy over time as an exogenous exposure with effects on the outcomes of obesity using data from the 1979 National Longitudinal Survey of Youth. Rather than examining heterogeneity of treatment effects only by basic demographic factors, we using an ensemble machine learning approach to examine whether treatment effects differ by several dozen potential demographic, socioeconomic, environmental and behavioral factors. 

Suggested readings:

Nancy Krieger.  Who and What Is a “Population”? Historical Debates, Current Controversies, and Implications for Understanding “Population Health” and Rectifying Health Inequities.  Milbank Q. 2012 Dec; 90(4): 634-681.

David H Rehkopf, William H Dow, Kate Strully.  The Effects of Anti-Poverty Tax Policy on Child and Adolescent BMT and Obsesity.

Kate W Strully, David H Rehkopf, and Ziming Xuan.  Effects of Prenatal Poverty on Infact Health: State Earned Income Tax Credits and Birth Weight.  American Sociological Reivew; Aug 2010; 75, 4; ProQuest, pp 534.