通过挖掘未观测的异质性因素，实现可解释的因果政策效果识别
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
- 将异质性处理效应识别转化为马尔可夫毯发现问题，利用多模态预处理表示
- 提出NEXIS方法，在非洲扶贫项目中结合卫星图像，发现新干预指南
- 结果兼具可解释性与可操作性，适合政策优化场景
异质性处理效应（HTE）识别对于解释干预影响并优化政策至关重要。现有方法在表达能力与可解释性之间权衡，若存在未测量的异质性驱动因素，则两端方法均可能导致无因果意义的虚假表征。本文聚焦于受控实验，认为得益于（i）更广泛的预处理测量（多模态、多视角）和（ii）低人工监督的可扩展表示，通过潜在交互因子实现因果性HTE表征已成可能。我们重新将HTE识别建模为充分且对齐的预处理表示上的马尔可夫毯发现问题，提出神经暴露交互搜索（NEXIS），一种具有理论保证且经实证验证的一致选择迭代算法。我们在非洲两项扶贫项目中部署NEXIS，分别引入卫星影像以捕捉先前未测量的环境调节因子，从而得出新颖、可解释且可指导后续优化的政策建议。
原文摘要 · Abstract (English)
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.
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