5 papers
Causal inference in connected populations with contagion
Subhankar Bhadra, Michael Schweinberger
Causal inference in connected populations is complicated by contagion and other real-world processes inducing dependence among outcomes. We address a gap in the literature on causa…
R Package iglm: Regression under Interference in Connected Populations
Cornelius Fritz, Michael Schweinberger
We introduce R package iglm, which implements a comprehensive framework for studying relationships among predictors and outcomes under interference. The implemented regression fram…
Scalable Sample-to-Population Estimation of Hyperbolic Space Models for Hypergraphs
Cornelius Fritz, Yubai Yuan, Michael Schweinberger
Hypergraphs are useful mathematical representations of overlapping and nested subsets of interacting units, including groups of genes or brain regions, economic cartels, political…
Causal Inference Under Network Interference
Subhankar Bhadra, Michael Schweinberger
We review recent advances in causal inference under interference, drawing on a complex and diverse body of work ranging from causal inference, network science, the health sciences,…
A regression framework for studying relationships among attributes under network interference
Cornelius Fritz, Michael Schweinberger, Subhankar Bhadra +1
To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and inter…