Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores
倾向得分传播：未知倾向得分下基于设计的推断框架AI 解读 ↓
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倾向得分传播：未知倾向得分下基于设计的推断框架
- 研究问题
- 当处理分配、抽样或缺失机制的概率未知时，如何把倾向得分估计的不确定性传入有限总体推断，而不对结局引入超总体假设？
- 方法与思路
- 作者提出倾向得分传播，以再生成与取并集程序衔接既有基于设计的方法，容纳参数和非参数倾向得分估计设置。
- 材料陈述的贡献
- 摘要称框架适用于多种基于设计的问题，理论和模拟显示能达到名义覆盖率，而部分既有方法出现明显覆盖不足。
- 阅读关注点
- 应核对再生成与并集的构造、倾向得分不确定性的刻画，以及覆盖保证条件、区间宽度和有限总体随机性来源。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明倾向得分估计及设计机制的条件，无结局超总体假设不意味着完全无假设。
原文摘要
Design-based inference, also known as randomization-based or finite-population inference, provides a principled framework for trustworthy statistical inference. It attributes randomness solely to the design mechanism, such as treatment assignment, survey sampling, or missingness, without imposing super-population distributional or modeling assumptions on the outcome data. From the seminal work of Fisher and Neyman to its recent resurgence, design-based inference has played a central role in causal inference, survey sampling, and missing data analysis. However, its use in many modern applications has been limited by a fundamental obstacle: existing design-based inference theory typically assumes that propensity scores (i.e., design probabilities) are known, whereas they are usually unknown in observational studies, real-world surveys, and missing data problems. We propose propensity score propagation, a general framework for valid design-based inference with unknown propensity scores. The framework uses a regeneration-and-union procedure to propagate uncertainty from propensity score estimation into downstream design-based inference, without introducing super-population assumptions about the outcomes. It accommodates both parametric and nonparametric propensity score settings, integrates seamlessly with existing design-based methods developed for known propensity scores, and applies broadly across design-based problems. Theoretical and simulation results show that the proposed framework achieves nominal coverage, even when existing approaches exhibit substantial under-coverage.