Dear Applied Statistics Workshop Community,
Our next meeting will be on November 8 (12:00 EST). Zeyang Jia presents
"Bayesian Safe Policy Learning with Chance Constrained Optimization:
Application to Military Security Assessment during the Vietnam War."
<When>
November 8, 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>
Algorithmic and data-driven decisions and recommendations are commonly used
in high-stakes decision-making settings such as criminal justice, medicine,
and public policy. We investigate whether it would have been possible to
improve a security assessment algorithm employed during the Vietnam War,
using outcomes measured immediately after its introduction in late 1969.
This empirical application raises several methodological challenges that
frequently arise in high-stakes algorithmic decision-making. First, before
implementing a new algorithm, it is essential to characterize and control
the risk of yielding worse outcomes than the existing algorithm. Second,
the existing algorithm is deterministic, and learning a new algorithm
requires transparent extrapolation. Third, the existing algorithm involves
discrete decision tables that are common but difficult to optimize over. To
address these challenges, we introduce the Average Conditional Risk
(ACRisk), which first quantifies the risk that a new algorithmic policy
leads to worse outcomes for subgroups of individual units and then averages
this over the distribution of subgroups. We also propose a Bayesian policy
learning framework that maximizes the posterior expected value while
controlling the posterior expected ACRisk. This framework separates the
estimation of heterogeneous treatment effects from policy optimization,
enabling flexible estimation of effects and optimization over complex
policy classes. We characterize the resulting chance-constrained
optimization problem as a constrained linear programming problem. Our
analysis shows that compared to the actual algorithm used during the
Vietnam War, the learned algorithm assesses most regions as more secure and
emphasizes economic and political factors over military factors.
<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