学习污染传输模型时，不同方法对因果推断结果影响大
Causal Inference under Interference with Learned Exposure Mappings
- 用四种方法学习污染传输过程，推导暴露映射
- 预测精度相似但因果效应估计差异达1.78至2.27
- 局部干预下差异更显著，适合环境政策评估者
在因果溢出分析中，暴露映射常被假设为已知。但在环境场景中，它们由未直接观测的传输过程决定，需从污染数据中学习。本文研究学习到的传输过程不确定性如何传播至暴露映射及下游溢出推断。比较了机制性传输模型与现代算子学习方法（包括PDE、PINO、FNO和GeoPT），基于模拟研究与加州PM₂.₅数据的实证分析。模拟中四类模型污染预测精度几乎相同，但估计的溢出效应在1.78至2.27之间波动；更准确恢复暴露映射的模型，其溢出估计更接近真实值。区域干预下差异较小，而点源局部干预下差异显著。加州数据分析显示，不同传输模型对观测PM₂.₅浓度预测相近，但对假设控污干预下的溢出效应推断差异明显。结果表明，仅靠预测一致性不足以保证因果推断可靠性，当暴露映射需学习时。
原文摘要 · Abstract (English)
Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induced by transport processes that are not directly observed and must instead be learned from pollution data. We study how uncertainty in learned transport processes propagates into exposure mappings and downstream spillover inference under interference. We compare mechanistic transport models with modern operator-learning approaches, including PDE, PINO, FNO, and GeoPT, using both simulation studies and an empirical analysis of California PM$_{2.5}$ data. In simulations, all four transport models achieved nearly identical pollution prediction accuracy, yet estimated spillover effects ranged from 1.78 to 2.27. Models that more accurately recovered the induced exposure mapping also produced spillover estimates closer to the true effect. Disagreement was modest for regional interventions but substantially larger for localized point-source interventions. The California analysis showed the same pattern: competing transport models produced similar predictions of observed PM${2.5}$ concentrations while implying different spillover effects under hypothetical pollution-control interventions. Our findings suggest that predictive agreement alone is insufficient for reliable causal inference when exposure mappings are learned rather than directly observed.
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