Shrinkage priors for Bayesian Substitute Confounders
贝叶斯替代混杂变量的收缩先验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要用多原因依赖学习替代混杂得分以估计平均潜在结局，因子模型服务于因果调整，未涉及宏观或金融因子分析。
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贝叶斯替代混杂变量的收缩先验
- 研究问题
- 多原因观测研究如何从原因间依赖学习低维替代混杂得分，同时避免过度编码处理向量、破坏重叠或捕获单一原因变化？
- 方法与思路
- 作者构建带收缩先验的贝叶斯因子分配框架，从后验集中、得分收缩与保持重叠的几何条件分析回归调整估计。
- 材料陈述的贡献
- 摘要称，在相关潜变量识别假设和理论条件成立时，估计量对平均潜在结局一致；模拟与 ADNI 应用展示稀疏得分及诊断。
- 阅读关注点
- 应核对潜变量识别假设、替代得分的充分性与重叠保持条件，以及收缩强度、单原因因子诊断和应用中的调整比较。
- 阅读边界
- 这是预印本，同行评审与期刊接收均未核实；摘要明确保留潜变量识别条件，不能将因子拟合或稀疏化本身当作消除混杂。
原文摘要
Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes. However, literal imputation of the unobserved confounder is often more complex than learning a lower-dimensional substitute score that preserves the shared assignment variation needed for stable causal adjustment. The deconfounder (Wang and Blei, 2019) and related substitute confounder methods exploit this idea, but flexible assignment models can fit the joint distribution of the causes while producing scores that over-encode the treatment vector, collapse overlap, or capture single-cause variation. We develop a Bayesian factor assignment framework for learning sparse substitute confounders that retain coarse multi-cause dependence with shrinkage priors. The theory is stated at the level of posterior concentration, factor score contraction, and overlap-preserving assignment geometry and therefore does not rely on a particular shrinkage prior. Under these conditions, the proposed regression-adjusted estimators are consistent for mean potential outcomes when the corresponding latent variable identification assumptions hold. Shrinkage priors provide a natural tool for latent structural learning: they favour low-dimensional factors supported by multiple causes, discourage effectively single-cause factors, and induce an ordering of the latent factors through progressive shrinkage. Synthetic experiments illustrate the roles of signal strength, outcome validity, and geometry-aware regularization. In an Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline analysis, sparse substitute scores recover much of the adjustment obtained by directly conditioning on invasive cerebrospinal-fluid biomarkers, while collapse diagnostics identify when fitted factors reduce to individual observed measurements.