A0881
Title: Estimating network-mediated causal effects via spectral embeddings
Authors: Keith Levin - University of Wisconsin (United States) [presenting]
Abstract: The last several years have seen a renewed and concerted effort to incorporate network data into standard regression analysis tools and make network-linked data legible to working scientists. Thus far, this literature has primarily developed tools to infer associative relationships between nodal covariates and network structure. statistical model is augmented for network regression with counterfactual assumptions. Under this model, causal effects can be partitioned into a direct effect not influenced by the network and an indirect effect induced by homophily. The method is a conceptually straightforward integration of latent variable models for networks into the well-known product-of-coefficients mediation estimator. This method is semi-parametric, easy to implement, and highly scalable.
