Designs for estimating the treatment effect in networks with interference

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Jagadeesan, R; Pillai, NS; Volfovsky, A

Published Date

  • January 1, 2020

Published In

Volume / Issue

  • 48 / 2

Start / End Page

  • 679 - 712

Electronic International Standard Serial Number (EISSN)

  • 2168-8966

International Standard Serial Number (ISSN)

  • 0090-5364

Digital Object Identifier (DOI)

  • 10.1214/18-AOS1807

Citation Source

  • Scopus