Robust Counterfactual Inference in Markov Decision Processes

Recent advancements in counterfactual inference for Markov Decision Processes (MDPs) have been unveiled by researchers tackling a significant challenge in the field. Traditional methods rely heavily on specific causal frameworks, often resulting in varied counterfactual interpretations. The authors introduce an innovative non-parametric technique that delineates accurate boundaries for counterfactual transition probabilities applicable across diverse causal models. This approach circumvents the complexities of solving extensive optimization problems, which can escalate with the size of the MDP, presenting instead simplified formulas that enhance both efficiency and scalability in managing intricate MDPs.