AI 中文总结
本研究开发了Riesz校准最优运输方法RICOT，用于将随机试验治疗效应迁移至真实世界人群，该方法具有双重稳健性，在模拟和真实案例中表现良好，优于部分传统方法。
AI 中文摘要
随机试验支持因果推断，但试验人群与目标人群之间的差异可能限制治疗效应对真实世界场景的可迁移性。许多现有方法会对试验参与的倾向进行建模，因此可能对模型误设和协变量分布的弱重叠敏感。最优运输（OT）提供了另一条路径，可直接在协变量空间中比较试验人群与目标人群。我们开发了RICOT，一种Riesz校准的OT程序，用于将治疗效应迁移至接受治疗的目标人群。我们考虑了一种带熵正则化的半不平衡OT，其中源边际被松弛。我们表明，未校准的OT会引入一种偏差，且该偏差不会随样本量增加而缩小。RICOT通过在运输问题中直接施加校准方程来消除这种偏差。随着校准筛的增长，校准权重一致估计目标-试验密度比，等价于目标期望泛函的Riesz表示，即使熵参数和源松弛参数保持固定为正。结合结局回归，所得估计量具有双重稳健性，并在适当的速率条件下达到半参数效率界。其方差可直接从影响函数估计，无需重采样或重复OT优化。模拟结果显示，在一系列重叠度和误设设置下，包括那些采样得分方法表现不佳的设置，RICOT的偏差低且覆盖度接近名义值。我们在一项涉及罕见进行性心肌病的真实世界应用中演示了RICOT，将传统的IPW和AIPW估计量与我们基于OT的IPW及双重稳健估计量进行比较，以将随机治疗效应迁移至接受相同治疗的真实世界人群。
英文摘要
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.
Comments86 pages, 7 figures, supplementary appendix included
