Confounder Detection via Treatment Intent: A New Observational Study Design
通过处理意向发现混杂因素：一种新的观察研究设计AI 解读 ↓
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通过处理意向发现混杂因素：一种新的观察研究设计
- 研究问题
- 当观察数据遗漏影响处理分配与结果的变量时，能否通过询问决策专家对匹配个体的处理意向差异，发现这些未观测混杂因素？
- 方法与思路
- 作者用匹配策略选择个体对，请专家解释处理决策差异，并以临床文本作为医生知识的代理，在已知真值的半合成环境验证构想。
- 材料陈述的贡献
- 摘要称，给出该研究设计能够引出未观测混杂因素的理论条件，并展示 ICU 电子病历存在混杂的证据与文本方法的概念验证。
- 阅读关注点
- 应核对匹配策略与专家询问的实施方式、发现混杂的理论条件，以及临床文本代理和半合成真值是否足以支持研究设计。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；具体理论条件未说明，概念验证不能保证发现全部混杂或识别真实政策效应。
原文摘要
Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many applied fields. However, RCTs are costly, time-consuming, and often constrained by ethical or practical limitations, motivating the need for causal methods able to draw conclusions from observational data. While such data is collected at ever larger scale, making its use for causal inference is often hindered by the fact that not all variables affecting treatment allocation and the outcome are observed: an issue known as unobserved confounding. In this paper, we introduce a new study design called confounder detection via treatment intent. The idea is to query a human expert who makes treatment decisions, and ask them to compare pairs of units proposed by a principled matching strategy, with the goal of eliciting unobserved variables that explain why treatment decisions differ. We provide a theoretical basis for such a procedure, ascertaining conditions under which such a study design may elicit unobserved confounders. Building on this newly established foundations, we study treatment effects of interventions in the intensive care unit (ICU). First, we show empirical evidence strongly indicating that electronic health records (EHRs) collected in ICUs are subject to unobserved confounding. By using clinical text notes as a proxy for physicians' knowledge and leveraging natural language processing, we provide a proof of concept for our methodology in a semi-synthetic environment with a known ground truth.