Using Transportability Estimators to Understand Reasons For Differences In Intervention Effects Across Sites

Notestein Seminar Series, Office of Population Research
Oct 25, 2022, 12:00 pm1:00 pm
300 Wallace Hall
  • Office of Population Research
  • Center for Statistics and Machine Learning
Event Description


Multi-site studies are common, and intervention effect estimates frequently differ across sites. We discuss reasons why effect estimates may differ across sites and relate these to transportability. In scenarios where transport is possible, we develop transport estimators to better understand why intervention effects differ across sites/population. We apply these estimators to motivating research questions from the Moving to Opportunity Study (MTO). MTO was a large-scale encouragement-design intervention in which Section 8 housing vouchers, which encourage families in public housing to move by subsidizing rents on the private market, were randomly assigned. In the context of MTO, we use the transport estimators in service of understanding the reasons for differences in site-specific effect estimates. These transport estimators may also be useful in other scenarios-including, to generate place-specific intervention effect estimates, in problems related to surrogacy, or in other data-fusion-related problems. We end with current work on extending these identification results and estimators to accommodate more general data structures.


I am an epidemiologist with research interests in developing and applying causal inference methods to understand social and contextual influences on mental health, substance use, and violence in disadvantaged, urban areas of the United States.

My current work focuses on developing methods for transportability and mediation, and subsequently applying those methods to understand how aspects of the school and peer environments mediate relationships between neighborhood factors and adolescent drug use across populations. More generally, my work on generalizing/ transporting findings from study samples to target populations and identifying subpopulations most likely to benefit from interventions contributes to efforts to optimally target available policy and program resources.

I completed a PhD in Epidemiology and an MHS in Biostatistics from the Johns Hopkins Bloomberg School of Public Health and was a Robert Wood Johnson Foundation Health and Society Scholar.

See original flier at this link.