提出新方法，让决策模型在不确定性中更稳健。
Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
- 用概率非确定性因果模型区分隐藏混淆与固有随机性
- 在脓毒症治疗模拟中验证，糖尿病状态为隐藏混杂因子
- 适合需要鲁棒决策的医疗、金融等高风险场景
序列决策中的反事实推断通常假设因果模型是确定性的，所有随机性源于潜在变量。然而，马尔可夫决策过程（MDPs）本质上是随机的。本文通过形式化基于概率非确定性因果模型的反事实策略优化，正确区分了潜在混杂因素与不可约随机性，并首次提出在敏感性分析框架下识别鲁棒反事实策略的实际优化问题。我们在一个脓毒症治疗模拟器上验证了该方法，其中糖尿病状态作为隐藏的全局混杂因子。实验表明，该方法能有效应对隐藏混淆带来的偏差，提升策略在不确定环境下的稳定性与可靠性。
原文摘要 · Abstract (English)
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.
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