Tag: confounders

Detecting Unobserved Confounders: A Kernelized Regression Approach arXiv:2601.00200v1 Announce Type: new Abstract: Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder Detection (KRCD), a novel method for…