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

Sadegh Shirani () and Mohsen Bayati ()
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Sadegh Shirani: a Operations, Information & Technology, Graduate School of Business , Stanford University , Stanford , CA 94305
Mohsen Bayati: a Operations, Information & Technology, Graduate School of Business , Stanford University , Stanford , CA 94305

Proceedings of the National Academy of Sciences, 2024, vol. 121, issue 40, e2322232121

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 treated unit’s impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.

Keywords: experiment design; network interference; total treatment effect; approximate message-passing (search for similar items in EconPapers)
Date: 2024
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