Statistics Graduate Student Seminar
October 14, 2022 @ 4:15 pm in MSC 1210
Abstract: The last several years have seen a renewed and concerted effort to incorporate network data into standard tools for regression analysis, and to make network-linked data legible to practicing scientists. Thus far, this literature has primarily developed tools to infer associative relationships between nodal covariates and network structure. In contrast, we augment a statistical model for network regression with counterfactual assumptions and show how causal effects on a network can be partitioned into a direct effect that is uninfluenced by the network, and an indirect effect that is induced by homophily.
After the talk I will talk about several directions for future work, and possibilities for collaboration, and then will be heading to the terrace.