Transporting Randomized Trial Effects to Real-World Populations via Riesz-Calibrated Optimal Transport
通过 Riesz 校准最优传输将随机试验效应迁移至现实人群AI 解读 ↓
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通过 Riesz 校准最优传输将随机试验效应迁移至现实人群
- 研究问题
- 当随机试验与现实目标人群的协变量分布不同、重叠较弱时，如何将试验治疗效应可靠地迁移至目标治疗人群？
- 方法与思路
- 作者在熵正则半非平衡最优传输中直接加入校准方程，构造密度比权重，并结合结局回归形成双重稳健估计。
- 材料陈述的贡献
- 摘要称校准可消除未校准传输的持久偏差，增长筛下权重一致，并在适当速率条件下达到半参数效率界。
- 阅读关注点
- 应核对效应迁移的识别条件、校准筛与固定正则参数的作用，以及密度比一致性和影响函数推断所需速率。
- 阅读边界
- 这是预印本，同行评审或期刊接收未经核验；摘要未列迁移识别条件和具体速率，模拟优势不能保证任意弱重叠下有效。
原文摘要
Randomized trials support causal inference, but differences between trial and target populations can limit the transportability of treatment effects to real-world settings. Many existing approaches model the propensity of trial participation and can therefore be sensitive to model misspecification and weak overlap of the covariate distributions. Optimal Transport (OT) offers a different route by comparing the trial and target populations directly in covariate space. We develop RICOT, a Riesz-calibrated OT procedure transporting treatment effects to a treated target population. We consider a semi-unbalanced OT with entropic regularization where the source marginals are relaxed. We show that the uncalibrated OT introduces a bias which does not shrink with increasing sample size. RICOT removes this bias by imposing calibration equations directly within the transport problem. With a growing calibration sieve, the calibrated weight consistently estimates the target-to-trial density ratio, equivalently the Riesz representer of the target expectation functional, even when the entropic and source-relaxation parameters remain fixed and positive. Combined with outcome regression, the resulting estimator is doubly robust and attains the semiparametric efficiency bound under suitable rate conditions. Its variance is estimated directly from the influence function, without resampling or repeated OT optimization. Simulations show low bias and near-nominal coverage across a range of overlap and misspecification settings, including settings in which sampling-score methods perform poorly. We illustrate RICOT in a real-world application involving a rare progressive cardiomyopathy, comparing conventional IPW and AIPW estimators with our OT-based IPW and doubly robust estimators for transporting the randomized treatment effect to a real-world population receiving the same treatment.