发表机构
Uttara University; Charles Sturt University; Australian Integrated Carbon (AiCarbon)（乌塔拉大学; 查尔斯史都华大学; 澳大利亚综合碳公司（AiCarbon））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GeoDose-CP提出图局部共形框架，联合建模处理偏移、雅可比与空间残差，实现连续处理下可靠的不确定性量化，并在基准与真实研究中验证有效性。
AI 中文摘要
当连续处理偏移、空间依赖性、有限支持和卫星结果不确定性必须同时解决时，从地球观测（EO）进行可靠的干预导向的不确定性量化仍然具有挑战性。现有的因果、共形和空间方法解决了该问题的部分内容，但它们的直接组合通常无法恢复适当的干预参考律，因为候选重分配共同改变了处理似然、标准化残差和图依赖残差似然。本研究提出了GeoDose-CP，一种在连续或混合连续-原子处理下用于局部随机潜在结果的支持感知共形框架。其核心方法论贡献是图局部目标轨道律，该律共同表示干预引起的处理偏移、逆结果尺度雅可比矩阵和空间残差依赖性。该框架进一步提供了精确的加权候选反演、具有显式差异核算的可扩展稀疏近似，以及在支持不足时的弃权（不执行）。评估使用了受控已知真值实验、MineDoseBench、处理密度敏感性分析、外部共形比较器以及一个多矿新南威尔士州（NSW）研究。在MineDoseBench中，GeoDose-CP在27种配置下实现了0.9692的平均选择性覆盖率，最小局部q0.05为0.8951；精确稀疏审计在2700个目标上产生了9个包含分歧。在NSW研究中，缺乏可审计的纵向康复处理使得处理依赖推断无法操作，而不是通过代理暴露强制推断。
英文摘要
Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial dependence, limited support, and satellite-outcome uncertainty must be addressed simultaneously. Existing causal, conformal, and spatial approaches address parts of this problem, but their direct combination does not generally recover the appropriate interventional reference law because candidate reassignment jointly alters treatment likelihood, standardized residuals, and graph-dependent residual likelihood. This study presents GeoDose-CP, a support-aware conformal framework for localized stochastic potential outcomes under continuous or mixed continuous-atomic treatment. Its central methodological contribution is a graph-local target-orbit law that jointly represents intervention-induced treatment shift, the inverse outcome-scale Jacobian, and spatial residual dependence. The framework further provides exact weighted candidate inversion, a scalable sparse approximation with explicit discrepancy accounting, and refusal under inadequate support. Evaluation used controlled known-truth experiments, MineDoseBench, treatment-density sensitivity analysis, external conformal comparators, and a multi-mine New South Wales (NSW) study. In MineDoseBench, GeoDose-CP achieved mean selective coverage of 0.9692 across 27 configurations and a minimum local q0.05 of 0.8951; exact-sparse auditing produced nine inclusion disagreements over 2,700 targets. In the NSW study, the absence of an auditable longitudinal rehabilitation treatment rendered treatment-dependent inference nonoperational rather than forcing inference through a proxy exposure.
