A1123
Title: The conflict graph design: Estimating causal effects under network interference
Authors: Christopher Harshaw - Columbia University (United States) [presenting]
Vardis Kandiros - Columbia University (United States)
Charilaos Pipis - MIT (United States)
Constantinos Daskalakis - MIT (United States)
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. A new experimental design is presented 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, but it also provides a unifying approach to designing network experiments. Consistent variance estimators and asymptotically valid confidence intervals are also provided, which facilitate inference of the causal effect under investigation.
