Causal message-passing for experiments with unknown and general network interference.

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 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 high-dimensional approximate message-passing methodology. It is tailored for multiperiod experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a t

SUBMITTER: Shirani S 

PROVIDER: S-EPMC11459125 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature