14 papers
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Ayush Khot, Miruna Oprescu, Maresa Schröder +2
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outco…
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…
Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
Kevin Valencia, Xihaier Luo, Shinjae Yoo +1
Physical fields are typically observed only at sparse, time-varying sensor locations, making forecasting and reconstruction ill-posed and uncertainty-critical. We present SOLID, a…
Dynamical Implicit Neural Representations
Yesom Park, Kelvin Kan, Thomas Flynn +4
Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge,…
OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
Kevin Valencia, Thilina Balasooriya, Xihaier Luo +2
Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts)…
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David K. Park, Xihaier Luo +2
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often…