Causal Message Passing: A Method for Experiments with Unknown and General Network Interference
arxiv(2023)
摘要
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, which we term causal message-passing, is grounded in
a high-dimensional approximate message passing methodology and is specifically
tailored to experimental design settings with prevalent network interference.
Utilizing causal message-passing, we present a practical algorithm for
estimating the total treatment effect and demonstrate its efficacy in four
numerical scenarios, each with its unique interference structure.
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