arXiv Statistics ML
Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation
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arXiv Machine Learning
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Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
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.
arXiv Statistics ML
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Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States
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 Machine Learning
Jul 16
CDS: Counterfactual Directionality Score for Structured Interventions in Spatial Graphs
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 Machine Learning
Aug 11
CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
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.
arXiv Machine Learning
Sep 16
Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
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 Machine Learning
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CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
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.