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One core assumption typically adopted for valid causal inference is that ofno interference between experimental units, i.e., the outcome of anexperimental unit is unaffected by the treatments assigned to otherexperimental units. This assumption can be violated in real-life experiments,which significantly complicates the task of causal inference as one mustdisentangle direct treatment effects from ``spillover'' effects. Currentmethodologies are lacking, as they cannot handle arbitrary, unknowninterference structures to permit inference on causal estimands. We present ageneral framework to address the limitations of existing approaches. Ourframework is based on the new concept of the ``degree of interference'' (DoI).The DoI is a unit-level latent variable that captures the latent structure ofinterference. We also develop a data augmentation algorithm that adopts ablocked Gibbs sampler and Bayesian nonparametric methodology to performinferences on the estimands under our framework. We illustrate the DoI conceptand properties of our Bayesian methodology via extensive simulation studies andan analysis of a randomized experiment investigating the impact of a cashtransfer program for which interference is a critical concern. Ultimately, ourframework enables us to infer causal effects without strong structuralassumptions on interference.

Degree of Interference: A General Framework For Causal Inference Under Interference