发表机构
DFKI Kaiserslautern; RPTU Kaiserslautern（德国人工智能研究中心凯泽斯劳滕分部; 凯泽斯劳滕工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CLAM是一种联合学习因果机制与分解映射的方法，可从粗粒度观测中估计局部因果效应，能捕捉空间变化的因果效应，适用于公共卫生等存在局部异质性的应用场景。
AI 中文摘要
从粗分辨率数据中学习细粒度空间模式颇具挑战性，尤其在因果场景下，需从聚合干预与结果中推断高分辨率效应。本文提出CLAM方法，该方法通过利用可调节这些效应的高分辨率上下文协变量，从粗观测中估计局部因果效应。CLAM联合学习因果机制与分解映射，能捕捉单独处理这些问题时遗漏的交互作用，支持局部效应估计、反事实推理及合理的结果分解，可在多样场景下可靠捕捉空间变化的因果效应，这对公共卫生、环境政策等应用尤为重要——此类应用虽存在显著局部异质性，却需在广泛尺度上制定决策，代码可访问此https URL。
英文摘要
Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes. We introduce CLAM, a method for estimating localized causal effects from coarse observations by exploiting high-resolution contextual covariates that modulate these effects. By jointly learning the causal mechanism and a disaggregation mapping, CLAM captures interactions that are missed when addressing these problems independently. The method supports localized effect estimation, counterfactual reasoning, and principled outcome disaggregation, and reliably captures spatially varying causal effects across diverse settings. This is particularly relevant for applications such as public health and environmental policy, where decisions are made at broad scales despite substantial local heterogeneity. Code is available at https://github.com/gerritgr/clam
