[gov3009-l] 9/13 Applied Stats: Steven Raudenbush

Dana Higgins danahiggins at fas.harvard.edu
Mon Sep 11 11:45:33 EDT 2017

Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Stephen
Raudenbush*, a Professor of Sociology at the University of Chicago. He will
be presenting work entitled *Estimands and Estimators for Multi-Site
Randomized Trials*.  Please find the abstract below.

As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided.  See you all there!

-- Dana Higgins

*Title:* *Estimands and Estimators for Multi-Side Randomized Trials*

*Abstract:* 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.
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