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stat.ML2026
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta +1
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which ca…
stat.ML2026
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Mariyam Khan, Shohei Shimizu, Thong Pham
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…