[Ml-stat-talks] Fwd: Reminder: CSML Seminar: Edoardo Airoldi, Tuesday, February 2, 2016 at 12:30pm | Computer Science Building, Room 105

Barbara Engelhardt bee at princeton.edu
Fri Jan 29 09:40:32 EST 2016


Talk of interest next week.

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Edoardo Airoldi-Harvard University

CSML Seminar

Tuesday, February 2, 2016

12:30pm-1:30pm

Computer Science Building, Room 105

**Lunch will be provided**



*Title*:  “Elements of causal inference on social, biomedical and
biological networks”



*Abstract*:  Estimating the causal effect of an intervention in a network
setting is the primary interest, and a major challenge, in many modern
endeavors at the nexus of science, technology and society. Examples include
HIV testing and awareness campaigns on mobile phones, improving healthcare
in rural populations using social interventions, promoting standard of care
practices among US oncologists on dedicated social media platforms, and
gaining a mechanistic understanding of cellular and regulation dynamics in
the cell.   A salient feature of these problems is that the response
measured on any one unit likely depends on the intervention given to other
units, a situation technically referred to as “interference” in the
parlance of statistics and machine learning. Importantly, the causal effect
of interference itself is often among the inferential targets of primary
interest.  Classical approaches to causal inference, however, largely rely
on the assumption of “lack of interference”, and/or on designing
experiments that limit the role interference, and are therefore untenable
in many modern endeavors.   In this talk, we will focus on technical issues
that arise in estimating causal effects when interference can be attributed
to a network among the units of analysis. We will outline new methodology
for making causal inferences in this setting, offer some theoretical
insights, and, time permitting, we will discuss strategies for optimal
experimental design that involve a piecewise constant approximation of a
certain graphon.





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