发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出广义BIC用于重加权分布中的模型选择，证明其一致性，并应用于边际结构模型中直接原因的选择，考虑了未测量混杂和权重估计，通过模拟验证了其渐近性质。
AI 中文摘要
基于贝叶斯信息准则（BIC）的评分方法常用于模型选择任务，如选择回归模型中的特征或从数据中学习有向无环图。在某些模型类别中，基于BIC的模型选择是一致的。然而，由于BIC基于观测到的似然函数，它不能直接应用于因果推断、缺失数据和领域偏移应用中出现的重加权分布。在此，我们提出了一种广义版本的BIC，允许在重加权分布中进行模型选择。我们证明了其相应的一致性性质，并展示了如何将其用于在边际结构模型中选择结果变量的直接原因。我们提出的方法考虑了存在未测量混杂因素以及权重必须从数据中估计的场景。通过模拟研究，我们展示了重加权BIC的渐近性质，并将其与替代的模型选择方法进行了比较。
英文摘要
Scoring methods based on the Bayesian information criterion (BIC) are commonly used for model selection tasks such as choosing features in a regression model or learning directed acyclic graphs from data. In certain model classes, model selection based on the BIC is consistent. However, since the BIC is based on the observed likelihood function, it does not apply directly to reweighted distributions arising in causal inference, missing data, and domain shift applications. Here, we propose a generalized version of the BIC that allows for model selection in reweighted distributions. We prove its corresponding consistency property and demonstrate how it can be used for selecting direct causes of an outcome variable in a marginal structural model. Our proposed method accounts for scenarios with unmeasured confounders and where the weights must be estimated from data. Through simulation studies, we demonstrate the asymptotic properties of the reweighted BIC and compare it with alternative methods for model selection.
Comments42 pages
