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April 2020 Designs for estimating the treatment effect in networks with interference
Ravi Jagadeesan, Natesh S. Pillai, Alexander Volfovsky
Ann. Statist. 48(2): 679-712 (April 2020). DOI: 10.1214/18-AOS1807

Abstract

In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment assignment as a “quasi-coloring” on a graph. Our idea of a perfect quasi-coloring strives to match every treated unit on a given network with a distinct control unit that has identical number of treated and control neighbors. For a wide range of interference functions encountered in applications, we show both by theory and simulations that the classical Neymanian estimator for the direct effect has desirable properties for our designs.

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Ravi Jagadeesan. Natesh S. Pillai. Alexander Volfovsky. "Designs for estimating the treatment effect in networks with interference." Ann. Statist. 48 (2) 679 - 712, April 2020. https://doi.org/10.1214/18-AOS1807

Information

Received: 1 August 2017; Revised: 1 December 2018; Published: April 2020
First available in Project Euclid: 26 May 2020

zbMATH: 07241565
MathSciNet: MR4102672
Digital Object Identifier: 10.1214/18-AOS1807

Subjects:
Primary: 62-02, 62K05, 91D30

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.48 • No. 2 • April 2020
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