[gov3009-l] Applied Statistics - 12/2 - Edo Airoldi

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Nov 30 10:21:04 EST 2015

Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Edo
Airoldi*, Professor of Statistics at Harvard University. He will be
presenting joint work with Don Rubin and Daniel Sussman entitled *Estimating
Causal Effects in the Presence of Interfering Units**.*  Please find the
abstract below and on the website

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website

-- Aaron Kaufman

Title: Estimating Causal Effects in the Presence of Interfering Units

Abstract: Classical approaches to causal inference largely rely on the
assumption of “lack of interference”, according to which the outcome of an
individual does not depend on the treatment assigned to others. In many
applications, however, such as designing and evaluating the effectiveness
of healthcare interventions that leverage social structure, assuming lack
of interference is untenable. In fact, the effect of interference itself is
often an inferential target of interest. In this talk, we will discuss
technical issues that arise in estimating causal effects when interference
can be attributed to a network among the units of analysis, and develop a
strategy for optimal experimental design in this context that involves a
piecewise constant approximation of a certain graphon.
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
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