Invited Talk by Chris Harshaw

Title: The Conflict Graph Design: Estimating Causal Effects Under Network Interference

Abstract: From political science and economics to public health and corporate strategy, the randomized experiment is a widely used methodological tool for estimating causal effects. In the past 15 years or so, there has been a growing interest in network experiments, where subjects are presumed to be interacting in the experiment and their interactions are of substantive interest. While the literature on interference has focused primarily on unbiased and consistent estimation, designing randomized network experiments to ensure tight rates of convergence is relatively under-explored. Not only are the optimal rates of estimation for different causal effects under interference an open question but previously proposed designs are created in an ad-hoc fashion. In this talk, I will present a new experimental design for network experiments called the "Conflict Graph Design" which, given a pre-specified causal effect of interest and the underlying network, produces a randomization over treatment assignment with the goal of increasing the precision of effect estimation. Not only does this experiment design attain improved rates of consistency for several causal effects of interest, it also provides a unifying approach to designing network experiments. We also provide conservatively consistent variance estimators and asymptotically valid confidence intervals which facilitate inference of the causal effect under investigation.

Joint work with Vardis Kandiros, Charis Pipis, and Costis Daskalakis at MIT.