Dear Applied Statistics Workshop Community,
Our last meeting of this semester will be on November 29 (12:00 EST). Yi
Zhang presents "Individualized Policy Evaluation and Learning under
Clustered Network Interference."
<When>
November 29, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
While there now exists a large literature on policy evaluation and
learning, much of prior work assumes that the treatment assignment of one
unit does not affect the outcome of another unit. Unfortunately, ignoring
interference may lead to biased policy evaluation and yield ineffective
learned policies. For example, treating influential individuals who have
many friends can generate positive spillover effects, thereby improving the
overall performance of an individualized treatment rule (ITR). We consider
the problem of evaluating and learning an optimal ITR under clustered
network (or partial) interference where clusters of units are sampled from
a population and units may influence one another within each cluster. Under
this model, we propose an estimator that can be used to evaluate the
empirical performance of an ITR. We show that this estimator is
substantially more efficient than the standard inverse probability
weighting estimator, which does not impose any assumption about spillover
effects. We derive the finite-sample regret bound for a learned ITR,
showing that the use of our efficient evaluation estimator leads to the
improved performance of learned policies. Finally, we conduct simulation
and empirical studies to illustrate the advantages of the proposed
methodology.
The most recent draft can be found here <https://arxiv.org/abs/2311.02467>.
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu