用粗粒度数据推断局部因果效应，提升政策制定精准度
CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
- 联合学习因果机制与空间拆解映射，捕捉局部交互
- 在多种场景下准确识别空间异质性因果效应
- 适合公共卫生与环境政策等需局部决策的领域
从粗分辨率数据中学习细粒度空间模式极具挑战性，尤其在因果设置下，需从聚合的干预与结果中推断高分辨率效应。本文提出CLAM方法，通过利用高分辨率上下文协变量来调节这些效应，从粗粒度观测中估计局部因果效应。该方法联合学习因果机制与拆解映射，捕捉独立处理时被忽略的交互作用。支持局部效应估计、反事实推理和合理的结果拆解，在多样场景中可靠地刻画空间变化的因果效应。这对公共健康与环境政策尤为重要，因决策常在宏观尺度进行，却面临显著的本地异质性。代码已公开于https://github.com/gerritgr/clam。
原文摘要 · Abstract (English)
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
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