4 papers
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…
Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
Jessica Lally, Milad Kazemi, Nicola Paoletti +2
Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables. However, Markov D…
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…
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…