作为因果模型的决策分析模型
Decision-analytical models as causal models
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中文总结 AI 辅助
研究聚焦健康经济评估中因果问题，核心方法是将决策分析模型过程形式化为因果推断任务，主要贡献是定义并分解模型偏差，为医学决策分析建模和因果推断提供统一基础，明确模型偏差及因果假设作用。
中文摘要 AI 辅助
健康经济评估本质上关注通过针对对比至少两种不同干预下成本和健康后果的估计量来回答因果问题。这需要各干预水平下潜在结果的联合分布，原则上可从观察到的健康结果联合分布中识别。但此类数据很少来自单一来源，促使使用决策分析模型直接近似各干预下结果的联合分布，其有效性取决于假设的可信度。本文将此过程明确形式化为因果推断任务，定义并分解决策分析模型偏差为模型结构偏差和输入参数偏差。决策分析模型常依赖缺乏直接可观测类似物的非常规目标参数，偏差会在模型中传播，即使在简单设置中也可能产生目标偏差。更广泛地说，本文为医学决策分析建模和因果推断提供统一基础，明确决策分析模型偏差的可能性及其因果假设的作用。最终，临床决策的可信度仅取决于其基础假设。
英文摘要
Health economic evaluations are fundamentally concerned with answering causal questions by targeting estimands that contrast the costs and health consequences that would be observed under at least two different interventions. This requires the joint distribution of potential outcomes under each level of intervention, which, with appropriate causal assumptions, can in principle be identified from the joint distribution of observed health outcomes. Such data, however, are rarely available from a single source. This limitation has motivated the use of decision-analytical models to approximate the joint distribution of outcomes under each intervention directly, informed by causal parameters drawn and synthesized from multiple sources, so that the potential outcomes of interest can be approximated as an expectation over the model-implied outcome trajectories. The validity of this approach, however, depends on the credibility of the underlying assumptions. In this work, we formalize this procedure explicitly as a task of causal inference, thereby defining and decomposing decision-analytical model bias into components arising from model structure (model bias) and input parameters (target bias). Because decision-analytical models often rely on unconventional target parameters lacking straightforward observable analogues, and because bias in these parameters can propagate through the model, target bias may arise even in simple settings, a point of central focus in this work. More broadly, this work provides a unifying foundation for medical decision-analytical modelling and causal inference, making explicit the potential for decision-analytical model bias and the role of causal assumptions contributing to it. Ultimately, the resulting clinical decision is only as credible as the assumptions underlying it.
发表机构
- Erasmus MC University Medical Center（伊拉斯谟斯大学医学中心）
- Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。
