arXiv:2510. 08762v2 Announce Type: replace Abstract: Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions.
The paper introduces DSP‑BART‑HS, a Dynamic Spatial Panel Bayesian Additive Regression Trees model with Horseshoe shrinkage, designed for high‑dimensional spatio‑temporal panel data. Across nine simulated scenarios, the model outperforms or matches a wide range of spatial econometric, non‑parametric machine learning, and small‑area estimators, especially when individual‑level non‑linearity drives outcome variance. The authors validate the method on two U.S. county‑level applications—intergenerational economic mobility and geographic income inequality—showing strong predictive accuracy even under unseen‑region, random, and temporal holdouts, while noting a temporal extrapolation advantage for a simpler autoregressive model.
arXiv:2607. 13508v1 Announce Type: new Abstract: Quantifying directional influence between node populations is a fundamental problem in graph-based modeling, particularly in spatial biological systems where cell-cell interactions shape functional outcomes.
arXiv:2608. 08064v1 Announce Type: new Abstract: 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.
The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.
arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.
GeoDose-CP introduces a graph‑local conformal inference framework for estimating localized stochastic potential outcomes when dealing with continuous or mixed continuous‑atomic treatments in Earth observation data. The method jointly models intervention‑induced treatment shifts, outcome‑scale Jacobians, and spatial residual dependence, and includes exact weighted candidate inversion, a scalable sparse approximation, and a refusal mechanism for inadequate support. Evaluation on controlled experiments, MineDoseBench, and a multi‑mine study in New South Wales demonstrates high selective coverage and identifies limitations when longitudinal treatment data are unavailable.
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.
arXiv:2606. 01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create loops or holes, generate branches, or reorganize an outcome cloud while leaving the average response nearly unchanged.
arXiv:2606. 06288v1 Announce Type: cross Abstract: Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements.
arXiv:2607. 10793v1 Announce Type: new Abstract: Numerical integration is a cornerstone of various scientific computing applications, such as engineering simulations and model evidence computations in probabilistic machine learning.