🤖 AI Summary
In causal inference, the no-interference assumption is frequently violated, complicating the separation of direct and spillover effects—particularly when interference structures are unknown and arbitrarily complex, rendering existing methods incapable of identification or valid inference. To address this, we introduce the *Degree of Interference* (DoI), a unit-level latent variable formalized for the first time as a tractable, estimable latent factor—enabling a general causal framework that imposes no prior assumptions on network topology or adjacency structure. Leveraging Bayesian nonparametrics, we develop a blocked Gibbs sampling data-augmentation algorithm that jointly infers DoI and treatment effects. Simulations and empirical analysis of a cash-transfer program demonstrate that our approach substantially improves spillover effect identification accuracy and delivers robust causal estimates even under completely unknown interference patterns.
📝 Abstract
One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is unaffected by 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 latent structure of interference. We also develop a data augmentation algorithm that adopts a blocked Gibbs sampler and 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 an analysis of a randomized experiment investigating the impact of a cash transfer program for which interference is a critical concern. Ultimately, our framework enables us to infer causal effects without strong structural assumptions on interference.
