Robust Counterfactual Inference in Markov Decision Processes

📰 ArXiv cs.AI

Learn to perform robust counterfactual inference in Markov Decision Processes, addressing limitations of existing methods by considering multiple causal models

advanced Published 25 May 2026

Action Steps

Identify the Markov Decision Process (MDP) and its observational and interventional distributions
Consider multiple causal models that align with the distributions
Apply robust counterfactual inference methods to account for model uncertainty
Evaluate the validity of the counterfactual distributions obtained from each causal model
Compare the results across different causal models to select the most appropriate one

Who Needs to Know This

Data scientists and ML researchers working with MDPs can benefit from this approach to improve the validity of their counterfactual inferences, and product managers can apply this to decision-making processes

Key Insight

💡 Robust counterfactual inference in MDPs requires accounting for model uncertainty by considering multiple causal models

Key Takeaways

Learn to perform robust counterfactual inference in Markov Decision Processes, addressing limitations of existing methods by considering multiple causal models

Full Article

Title: Robust Counterfactual Inference in Markov Decision Processes

Abstract:
arXiv:2502.13731v5 Announce Type: replace Abstract: 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 counterfactuals identifiable. However, there are usually many causal models that align with the observational and interventional distributions of an MDP, each yielding different counterfactual distributions, so fixing a particular causal model limits the validity (and

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