Semi-supervised inference using unlabeled summary statistics
利用无标签汇总统计量的半监督推断AI 解读 ↓
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利用无标签汇总统计量的半监督推断
- 研究问题
- 当无标签样本只能提供均值与协方差等汇总信息时，如何改善均值估计、纠正标签选择偏差，并推广平均治疗效应？
- 方法与思路
- 作者构造仅对有标签数据进行样本分割的交叉拟合方法，分别处理标签与协变量独立或相关的情形，并扩展至治疗效应估计。
- 材料陈述的贡献
- 摘要称，无标签汇总量仍可改善效率与选择偏差，方法适用于高维并对模型误设稳健，且在相应稀疏条件下支持有效推断。
- 阅读关注点
- 应核对汇总量需要包含哪些信息、标签选择与总体差异的条件，以及交叉拟合、稀疏限制和治疗效应迁移的识别依据。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未经核实。摘要未说明具体稀疏率、汇总量精度要求或稳健性与可迁移性的边界。
原文摘要
Semi-supervised inference assumes access to a labeled dataset together with a large unlabeled dataset in which the outcome variable is missing, and it is widely used to improve statistical efficiency and support generalizability across populations. In many modern applications, however, individual-level unlabeled data may not be directly accessible due to privacy restrictions, data-sharing limits, or storage constraints, while summary statistics such as sample means and covariances from the unlabeled population are often available. In this work, we study this constrained semi-supervised setting where, in addition to labeled data with individualized observations, auxiliary information from the unlabeled population is available only through summary statistics. We propose new semi-supervised inference methods for mean estimation under both covariate-independent and covariate-dependent labeling and show that unlabeled summaries can still improve efficiency and help correct selection bias. The proposed methods apply in high dimensions and are robust to model misspecification. Valid inference is obtained under sparsity conditions comparable to those required by semi-supervised methods that assume access to individual-level unlabeled samples. Our approach relies on a specialized cross-fitting procedure, where sample splitting is applied only to the labeled data, which removes the need for individualized unlabeled covariates. We further extend this framework to average treatment effect estimation, enabling generalizability and transportability of causal conclusions in this constrained semi-supervised setting.