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Understanding reasons for differences in intervention effects across sites

PRIISM seminar speaker Kara Rudolph, Assistant Professor, Columbia University
(1 hour)

A seminar by Kara Rudolf, Assistant Professor, Columbia University

Wednesday, September 16, 2020

11:00 am - 12:00 pm EST via Zoom*

Watch Recording of Kara Rudolph's seminar


Multi-site interventions are common in public health, public policy, and economics. Do we expect an intervention effect in one site to be the same as the intervention effect in another site? In many cases, we would answer “no”. First, there could be differences in site-level variables related to intervention design/implementation or contextual variables, like the economy, that would modify intervention effectiveness. Such variables suggest that the intervention either is not the same or does not work the same in the two sites. Second, there could be differences in person-level variables—population composition—across sites that also modify intervention effectiveness. There could also be differences in the mechanisms producing intermediate variables on the pathway from the intervention to the outcome. The latter two reasons could cause intervention effects to differ across sites even if the interventions are structured and implemented in an identical fashion. An example of this, which we use to motivate this work, is from the Moving to Opportunity (MTO) trial. MTO is a five-site, encouragement-design intervention in which families in public housing were randomized to receive housing vouchers and logistical support to move to low-poverty neighborhoods. When we started this work, there had been no quantitative examination of the underlying reasons for differences in MTO’s effects across sites. We propose doubly robust and efficient estimators to predict total, direct, and indirect effects of treatment in a new site based on data in source sites. The extent to which these predicted estimates correspond with the observed estimates can shed light on reasons for site differences in intervention effects.


Kara Rudolph is 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. Her 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, her 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. She 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.