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A resampling approach for causal inference on novel two-point time-series with application to identify risk factors for type-2 diabetes and cardiovascular disease

Xiaowu Dai · Saad Mouti · Marjorie Lima do Vale · Sumantra Ray · Jeffrey Bohn 等 6 位作者首次提交 2021-03-12 · 修订 2023-01-17 · v2arXiv预印本

无对照组两时点数据的重抽样因果推断及健康风险应用AI 解读

Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type- $2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called 'I-Rand' for independently sampling one of the two time points for each…Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called 'I-Rand' for independently sampling one of the two time points for each…

微观 · 因果推断因子模型时间序列

关联记录被引 0 次 ↗OpenAlex · 核验 2026-09-30

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