2 papers
stat.ML2026
Detecting Unobserved Confounders: A Kernelized Regression Approach
Yikai Chen, Yunxin Mao, Chunyuan Zheng +7
Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous…
physics.comp-ph2025
Extracting Interaction Kernels for Many-Particle Systems by a Two-Phase Approach
Yangxuan Shi, Wuyue Yang, Liu Hong
This paper presents a two-phase method for learning interaction kernels of stochastic many-particle systems. After transforming stochastic trajectories of every particle into the p…