Events
FDS Co-sponsored Event
Designing Spatial Treatments
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Speaker: Michael Leung (UCSC) Associate Professor University of California Santa Cruz Friday, October 2, 2026 12:00PM - 1:00PM Lunch in 1307 at 11:30am
Talk in 1327 at 12:00pm Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=9bf4925b-170b-45c2-af4e-b4cd00de373d |
Abstract: Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit’s response diminishes with distance to a treated site. Our estimand of interest is an “uncontaminated” effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Matérn point process which separates treatments by a distance of at least r. A larger choice of r reduces bias by separating interventions but increases variance by reducing their numerosity. We choose r to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.
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