Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables 事件

Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables arXiv:2606.08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM). Existing methods that handle hidden confounders typically assume additive noise, but in practice, causes often modulate not just the mean but also the variance of their effects. We prove that acyclic di