Degree of Interference: A General Framework For Causal Inference Under Interference
Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi

TL;DR
This paper introduces a novel framework based on the 'degree of interference' to enable causal inference in experiments where interference between units occurs, overcoming limitations of existing methods.
Contribution
It proposes a new latent variable model for interference, along with a Bayesian inference algorithm, allowing causal analysis without strict structural assumptions.
Findings
Framework effectively captures arbitrary interference structures.
Bayesian method performs well in simulations and real data.
Enables causal inference under complex interference scenarios.
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 the number of potential outcomes increases, it becomes challenging to 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…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Bayesian Inference · Bayesian Modeling and Causal Inference
