Minimax-Optimal Static Estimators Under Interference

Arthur Delarue and Ilan Lobel

Papers from arXiv.org

Abstract: We study the problem of estimating the global treatment effect in randomized experiments under interference, where a unit's outcome may depend on the treatment assignments of other units. We adopt a minimax framework, allowing an adversary to select an outcome function from a class of functions that satisfy bounded outcomes and bounded interference. We derive minimax-optimal static estimators that are robust to a wide range of interference structures, parameterized by a scalar $\delta$ controlling the maximum influence of one unit's treatment on another's outcome. Under large interference, we observe two regimes. When the treatment and control groups are equally sized, the minimax-optimal static estimator is the zero estimator. When one of the two groups is larger, the minimax-optimal static estimator is a "half-zero" estimator, in which the outcomes of the smaller group are ignored, and the estimator only considers the outcomes of the larger group.

Date: 2026-11
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2611.02048 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:2611.02048

Access Statistics for this paper

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