AI 中文总结
研究在离线强化学习用于得出最优治疗建议时隐藏混杂因素/模型错误指定情况，采用协变量平衡诊断，发现现有研究有偏差风险或评估指标不足，结论给出未来使离线RL在治疗建议应用更稳健的研究方向。
AI 中文摘要
本文探讨了协变量平衡诊断在应用离线强化学习（RL）得出最优治疗建议的研究中，用于检测隐藏混杂因素/模型错误指定情况的应用。结果表明，现有离线RL治疗建议研究存在高偏差风险，或现有协变量平衡指标不足以评估此类研究。无论如何，现有离线RL研究在统计上都不稳健。结论提出了未来研究方向，以实现更具方法稳健性的离线RL在治疗建议问题上的应用。
英文摘要
This article explores the application of covariate balance diagnostics for detecting the presence of hidden confounding/model miss-specification in studies applying offline reinforcement learning (RL) to deriving optimal treatment recommendations. The results demonstrate that, either there is a high risk of bias within existing offline RL studies for treatment recommendations and/or, existing covariate balance metrics are not sufficient to assess such studies. Regardless, existing offline RL studies cannot be concluded as being statistically robust. The conclusions propose future research directions for obtaining more methodologically robust applications of offline RL to treatment recommendation problems.
