标题： 从观察性数据到临床建议：估计个体治疗效应和学习策略的因果框架 显示英文标题

标题： From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies

摘要： 我们提出一个框架，用于构建患者特定的治疗推荐模型，该框架基于近年来关于学习患者层面因果模型的大量文献，并受到Hernan和Robins的目标试验范式的启发。 我们关注安全性和有效性，包括在使用观察性数据时至关重要的因果识别问题。 我们不提供具体的模型，而是提供一种将现有方法和专业知识整合到实际流程中的方式。 我们进一步提供了一个现实世界的应用案例，即对在住院期间发生急性肾损伤的心力衰竭患者进行治疗优化。 结果表明，我们的流程可以改善患者的预后，优于当前的治疗方案。 显示英文摘要

摘要： We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real world use-case of treatment optimization for patients with heart failure who develop acute kidney injury during hospitalization. The results suggest our pipeline can improve patient outcomes over the current treatment regime.