Designs for estimating the treatment effect in networks with interference
Ravi Jagadeesan, Natesh S. Pillai, Alexander Volfovsky
The Annals of Statistics · 2020 · Volume 48 · Issue 2
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.
Keywords
62-02, 62K05, 91D30, Experimental design, homophily, network interference, Neyman estimator, symmetric interference model
Enable JavaScript for citation tools and additional paper interactions.