Doubly Robust Estimators of Quantile Treatment Effects With Semiparametric Cumulative Probability Models
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基于半参数累积概率模型的双重稳健分位数处理效应估计
- 研究问题
- 当结局偏斜或受检测限影响时，如何减少分布模型误设对分位数处理效应估计的影响，并开展可靠的统计推断？
- 方法与思路
- 作者结合秩型半参数累积概率模型与有效影响函数，分别通过反演潜在结局边际分布和直接求解分位数方程构建估计量。
- 材料陈述的贡献
- 摘要称估计量具有双重稳健性与渐近正态性，并扩展至概率处理效应及条件版本；模拟支持部分方差估计方法的有限样本表现。
- 阅读关注点
- 核对因果识别假设、双重稳健所对应的两类模型、检测限处理及分位数插值方式，区分点估计和方差估计的稳健条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出正则条件及检测限机制，不能据此确认所有误设情形下推断均有效。
原文摘要
The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distribution can provide additional information in biomedical research. Quantile treatment effect (QTE) captures such distributional differences, particularly when outcomes are skewed. However, existing approaches for estimating QTE make distributional assumptions about the outcome and are thus sensitive to model misspecification. Motivated by an HIV study with skewed outcomes, one of which is subject to detection limits, we propose a doubly robust framework for estimating QTE based on the cumulative probability model (CPM), which is a rank-based, semiparametric linear transformation model. We develop two CPM-based estimation strategies: (1) an inverse-cumulative distribution function (CDF) approach that first estimates the marginal CDF of potential outcomes using the efficient influence function (EIF) and then obtains marginal quantiles via weighted quantile interpolation by inverting the distribution, and (2) a direct approach that solves the EIF of potential marginal quantiles. The proposed estimators are doubly robust and asymptotically normal. We further extend the framework to probability treatment effects (PTEs) and their conditional counterparts. For statistical inference, we investigate several variance estimation procedures, including EIF-based estimators, sandwich estimators, and the nonparametric bootstrap. Simulation studies illustrate that the empirical sandwich estimator and the nonparametric bootstrap provide doubly robust variance estimation with stable finite-sample performance under nuisance model misspecification. The proposed methods are evaluated through extensive Monte Carlo simulations and illustrated using an HIV data application.