1 citations · 1 across the 6 of their papers we have counts for
6 papers
LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation
Zijian Shen, Taijie Chen, Bin Zhou +2
Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly. A practical but underexplo…
Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimation
Yiling Liu, Juncheng Dong, Chen Fu +4
Estimating counterfactual outcomes from time-series observations is crucial for effective decision-making, e.g. when to administer a life-saving treatment, yet remains significantl…
Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
Ziyang Jiang, Zach Calhoun, Yiling Liu +2
Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integ…
Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
Ian Hough, Loïc Argentier, Ziyang Jiang +6
Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Sinc…
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
Lei Duan, Ziyang Jiang, David Carlson
Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dat…
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
Ziyang Jiang, Yiling Liu, Michael H. Klein +5
Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is imp…