DESIGNS FOR ESTIMATING THE TREATMENT EFFECT IN NETWORKS WITH INTERFERENCE

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 gr...

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Published in:The Annals of statistics 2020-04, Vol.48 (2), p.679-712
Main Authors: Jagadeesan, Ravi, Pillai, Natesh S., Volfovsky, Alexander
Format: Article
Language:English
Subjects:
Causality
Coloring
Econometrics
Estimating techniques
Graph theory
Interference
Probability
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description 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.
doi_str_mv 10.1214/18-AOS1807
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subjects Causality
Coloring
Econometrics
Estimating techniques
Graph theory
Interference
Probability
title DESIGNS FOR ESTIMATING THE TREATMENT EFFECT IN NETWORKS WITH INTERFERENCE
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