10/6/22 | 4:15pm | E51-145

Christina Lee Yu

Assistant Professor
Cornell

Abstract: In many domains, we are interested in estimating the total causal treatment effect in the presence of network interference, where the outcome of one individual or unit is affected by the treatment assignment of those in its local network. Additional challenges arise when complex cluster randomized designs are not feasible to implement, or the network is unknown and costly to estimate. We propose a new measure of model complexity that characterizes the difficulty of estimating the total treatment effect under the standard A/B testing setup. We provide a class of unbiased estimators whose variance is optimal with respect to the population size and the treatment budget. Furthermore, we show that when the network is completely unknown, we can still estimate the total treatment effect under a richer yet simple staggered rollout experimental design. The proposed design principles, and related estimator, work with a broad class of outcome models. Our solution and statistical guarantees do not rely on restrictive network properties, allowing for highly connected networks that may not be easily clustered.

This is joint work with Edoardo Airoldi, Christian Borgs, Jennifer Chayes, Mayleen Cortez, and Matthew Eichhorn, based on the manuscripts https://arxiv.org/abs/2208.05553, https://arxiv.org/abs/2205.14552, and https://arxiv.org/abs/2205.12803.

Bio: Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering. Prior to Cornell, she was a postdoc at Microsoft Research New England. She received her PhD in 2017 and MS in 2013 in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in the Laboratory for Information and Decision Systems. She received her BS in Computer Science from California Institute of Technology in 2011. She received honorable mention for the 2018 INFORMS Dantzig Dissertation Award. She is a recipient of the 2021 Intel Rising Stars Award and a JPMorgan Faculty Research Award. Her research interests include algorithm design and analysis, high dimensional statistics, inference over networks, sequential decision making under uncertainty, online learning, and network causal inference.