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在真实实验中将因果消息传递与网络感知方法比较验证AI 解读

Estimating total treatment effects in the presence of network interference typically requires knowledge of the underlying interaction structure. However, in many practical settings, network data is either unavailable, incomplete, or measured with substantial error. We demonstrate that causal message passing, a methodology that leverages temporal structure in outcome data rather than network topology, can recover total treatment effects comparable to network-aware approaches.…

存在溢出与部分资格限制的二部实验：总处理效应估计AI 解读

We study randomized experiments in bipartite systems where only a subset of treatment-side units are eligible for assignment while all units continue to interact, generating interference. We formalize eligibility-constrained bipartite experiments and define estimands aligned with full deployment: the Primary Total Treatment Effect (PTTE) on eligible units and the Secondary Total Treatment Effect (STTE) on ineligible units. Under randomization within the eligible set, we give…