A Bayesian nonparametric approach for detecting interference and estimating causal effects

Music City Center 

Classical methods of causal inference typically assume that an experimental intervention influences solely the unit receiving it and does not interfere with the behavior of any other unit. However, it is becoming increasingly common for experiments to contravene this assumption. Estimating causal effects in the presence of treatment interference necessitates an understanding of the dynamics between units and their influence on others' responses. In this study, we consider estimation under the recently proposed K Nearest Neighbor Interference Model (KNNIM), which assumes that a unit's response is influenced by its treatment status and the treatments administered to its K "closest" units. We broaden the KNNIM framework to the scenario where multiple (non-identical) experiments are performed on the same set of units. We develop a novel approach that combines an infinite beta-Bernoulli process Bayesian linear model with the KNNIM framework to allow for the simultaneous discovery of the correct K and accurate estimation of treatment effects. We demonstrate the usefulness of the approach in identifying treatment interferences through simulations.

Causal Inference

Treatment Interference

K Nearest Neighbor Interference Model (KNNIM)

Bayesian Nonparametric 

Section on Statistics in Epidemiology