Abstract: Causal inference under interference provides a formal framework for measuring network effects using randomized experiments. Experimental designs based on graph cluster randomization (GCR), randomizing units at the level of network clusters, have been shown to greatly reduce variance when estimating treatment effects in networks, compared to unit-level random assignment. But even so the variance is very often prohibitively large. This talk will review graph cluster randomization and propose a randomized version of the GCR design, descriptively named randomized graph cluster randomization (RGCR), which uses a random clustering rather than a single fixed clustering. By considering an ensemble of many different cluster assignments, this design avoids a key problem with GCR where a given unit is sometimes “lucky” or “unlucky” in a given clustering, thereby greatly reducing the variance of network treatment effect estimators in both theory and across extensive simulations.

Bio: Johan Ugander is an Associate Professor at Stanford University in the Department of Management Science & Engineering, within the School of Engineering. His research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale social and behavioral data. Prior to joining the Stanford faculty he was a postdoctoral researcher at Microsoft Research Redmond 2014-2015 and held an affiliation with the Facebook Data Science team 2010-2014. He obtained his Ph.D. in Applied Mathematics from Cornell University in 2014. His awards include a NSF CAREER Award, a Young Investigator Award from the Army Research Office (ARO), three Best Paper Awards (2012 ACM WebSci Best Paper, 2013 ACM WSDM Best Student Paper, 2020 AAAI ICWSM Best Paper), and the 2016 Eugene L. Grant Undergraduate Teaching Award from the Department of Management Science & Engineering.

This talk is hosted by the ISL Colloquium.