2 papers
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…
cs.AI2026
Planning under Distribution Shifts with Causal POMDPs
Matteo Ceriscioli, Karthika Mohan
In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the d…