Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
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Computer Science > Machine Learning
Title:Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
Abstract:Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables. However, Markov Decision Processes (MDPs) are inherently stochastic. We address this by formalising counterfactual policy optimisation under probabilistic nondeterministic causal models, which properly separates latent confounding from irreducible stochasticity, and here propose a first practical optimisation problem for identifying robust counterfactual policies under a sensitivity analysis framework. We validate our approach on a sepsis treatment simulator, where diabetes status acts as a hidden global confounder.
| Comments: | Accepted at UAI 2026 Workshop on Causality for Decision Making |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.02893 [cs.LG] |
| (or arXiv:2608.02893v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02893
arXiv-issued DOI via DataCite (pending registration)
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