From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
中文标题: 从Token到策略：可因果解释的异质处理效应识别
英文摘要
The paper introduces NEXIS, a method for identifying heterogeneous treatment effects (HTEs) in controlled experiments by re-framing the problem as Markov-blanket discovery on sufficient, aligned multi-modal pre-treatment representations. NEXIS iteratively selects latent interactors with provably consistent selection, avoiding spurious causal characterizations that arise from unmeasured effect modifiers. The approach is deployed on two anti-poverty programs in Africa, augmenting each with satellite imagery to capture previously unmeasured environmental modifiers. The results produce novel, interpretable prescriptive guidelines for optimizing the programs' next iterations.
中文摘要
本文提出NEXIS方法，通过将异质处理效应（HTE）识别重构为在充分对齐的预训练多模态表示上进行马尔可夫毯发现，从而避免未观测效应修饰因子导致的虚假因果特征。NEXIS以可证明的筛选一致性迭代选择潜在交互因子。该方法被应用于非洲两个反贫困项目，结合卫星图像捕捉此前未测量的环境修饰因子，生成了可解释的优化指南以改进项目下一轮实施。
关键要点
NEXIS re-frames HTE identification as a Markov-blanket discovery problem on aligned multi-modal pre-treatment representations.
NEXIS将对齐的多模态预治疗表示上的HTE识别重构为马尔可夫毯发现问题。
The method provides provable selection consistency, preventing spurious causal characterization from unmeasured heterogeneity drivers.
该方法提供可证明的筛选一致性，避免未测量异质性驱动因素导致的虚假因果特征化。
Applied to African anti-poverty programs with satellite imagery, it uncovered novel environmental effect modifiers and generated prescriptive policy guidelines.
结合卫星图像应用于非洲反贫困项目，揭示了新的环境效应修饰因子并生成可操作的策略指南。