Zhongyi Que

MDL Meets Latent Confounders: LNML-based Causal Discovery

Zhongyi Que, Shin Matsushima, Kenji Yamanishi

Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting their applicability. We propose an MDL-based causal discovery framework that explicitly accounts for lat…Read more