这篇论文用NEXIS方法在非洲扶贫数据上找到了新的环境因子，能指导政策迭代，因果推断爱好者可以看看。
该论文提出NEXIS（Neural Exposure Interaction Search），一种迭代方法，用于在可控实验中从多模态预处理表示中识别异质处理效应的因果结构。NEXIS将HTE识别重新定义为马尔可夫毯发现问题，并提供了可证明的一致性选择保证。作者将NEXIS应用于非洲两项扶贫项目，结合卫星影像捕捉未测量的环境效应修饰因子，得到了新的、可解释的政策优化指南。实验验证了NEXIS在合成数据和实际数据上的有效性。
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivity for interpretability, but, if some active heterogeneity drivers are unmeasured, methods at both ends of this spectrum allow for spurious HTE characterization with no causal reading. In this work, we focus on controlled experiments and argue that an oracle HTE causal characterization via the latent interactors is now within reach, thanks to (i) more extensive pre-treatment measurements, i.e., multi-modal and multi-view, and (ii) scalable representations with minimal human supervision. We then re-frame HTE identification as a Markov-blanket discovery problem on a sufficient and aligned pre-treatment representation, and introduce Neural EXposure Interaction Search (NEXIS), an iterative procedure with provable and empirically validated consistent selection. We deploy NEXIS on two anti-poverty programs in Africa, augmenting each with satellite imagery capturing previously unmeasured environmental effect modifiers, leading to novel, interpretable and prescriptive guidelines to optimize the programs' next iterations.