Optimal Treatment Assignment Rules on Networked Populations

Abhishek Ananth

Papers from arXiv.org

Abstract: I analyze decision problems where a planner must choose, based on data from an experiment in a pilot population, how to simultaneously distribute a limited number of treatments that generate network spillovers across individuals in a target population. A global treatment rule maps the target population's observed covariates and network features into a jointly feasible allocation. Under local-response, stability, support, and policy-relevant overlap conditions, I identify the resulting welfare using a transported inverse propensity weighted criterion and construct an empirical welfare maximizer. I derive a finite-sample regret bound under restrictions on the complexity of network spillovers and the complexity of the global rule class, measured by its Natarajan dimension. The upper bound uses independent node-level variation, dyadic network formation without imposing a bounded maximum degree. A minimax lower bound establishes a corresponding rate comparison under additional welfare-relevant richness. Finally, I compare oracle optimal global and local rules (which allocate treatments on an individual-by-individual basis) and bound the welfare gap between them. An empirically calibrated oracle comparison illustrates the magnitude of this welfare gap.

Date: 2026-10
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2610.32735 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2610.32735

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().