Causal Message Passing: A Method for Experiments with Unknown and General Network Interference

arXiv:2311.08340v2 Announce Type: replace-cross
Abstract: Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a new framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in …

abstract arxiv causal data data-driven decisions evaluation general impacts interference methodology network replace stat.me stat.ml treatment type units