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cs.AI2026
Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties
Jessica Lally, Milad Kazemi, Nicola Paoletti
Counterfactual reasoning (asking what would have happened, had a decision been different) is a powerful tool for identifying causes of failure and explaining the behaviour of AI sy…
cs.AI2025
Robust Counterfactual Inference in Markov Decision Processes
Jessica Lally, Milad Kazemi, Nicola Paoletti
This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make c…
cs.AI2024
Counterfactual Influence in Markov Decision Processes
Milad Kazemi, Jessica Lally, Ekaterina Tishchenko +2
Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs). Given an MDP path , this kind of inference allows us to…