Papers for

mining rehabilitation planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Graph-local method improves uncertainty in earth observation treatments

GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

Abstract: 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.

Thu 24 SeptMachine Learning
The gist
Measuring how changes in continuous environmental treatments affect outcomes using satellite data is difficult because of complex location-dependent factors and uncertainty in measurements. The authors created GeoDose-CP, a method that carefully accounts for where data are supported, local relationships, and treatment levels to better estimate the uncertainty of effects. This approach avoids misleading conclusions when data is sparse or treatments vary smoothly across space. They tested it with simulated experiments and real mining rehabilitation data in Australia, showing more reliable uncertainty quantification than existing methods.
Open → 2609.28895v1