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

One core assumption that is consistently adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is not a function of the treatments assigned to other experimental units. This assumption can be violated in real-life experiments, which significantly complicates the task of causal inference as one must disentangle direct treatment effects from ``spillover'' effects. Current methodologies are lacking, as they cannot handle arbitrary, unknown interference structures to permit inference on causal estimands. We present a general framework to address the limitations of existing approaches. Our framework is based on the new concept of the ``degree of interference'' (DoI). The DoI is a unit-level latent variable that captures the spillover effects on a unit. This concept can flexibly accommodate any interference structure. We also develop a Bayesian nonparametric methodology to perform inferences on the estimands under our framework. We illustrate the DoI concept and properties of our Bayesian methodology via extensive simulation studies and a real-life case study from additive manufacturing for which interference is a critical concern in achieving geometric accuracy control. Ultimately, our framework enables us to infer causal effects without strong structural assumptions on interference.