Applied Stats Workshop (Gov 3009)


Wednesday, September 13, 2017, 12:00pm to 1:30pm


CGIS Knafel K354

The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.  Free lunch is provided.

Stephen Raudenbush Presents. 




Estimands and Estimators for Multi-Site Randomized Trials

Stephen W. Raudenbush University of Chicago

Presented at the Harvard Applied Statistics Seminar September 13, 2017

(Joint work with Daniel Schwartz, University of Chicago)

In a multi-site randomized trial, sites such as schools or hospitals are sampled; within each site, persons are assigned at random to treatments. Such studies are increasingly common in social welfare, medicine, and education. In this talk, I’ll first use potential outcomes and a super- population framework to precisely describe different potential populations and parameters of interest, which may diverge considerably when treatment effects vary. Second, I’ll show that maximizing a weighted two-level likelihood produces consistent estimators of all parameters, but only after we introduce a correction for estimating between-site variance components. Third, we’ll see that these weighted estimators, while consistent, may be embarrassingly inefficient (to the point of being improved by throwing out data). Precision weighting may help but may introduce large-sample bias. In the interest of time, I will focus on two parameters: (1) the average impact of treatment assignment (“intention to treat effect”); (2) in trials with non- compliance, the average impact of participation in the treatment on those induced by random assignment to participate (“complier average causal effect”). I’ll illustrate with data from the National Head Start Impact Study.