针对纵向数据中罕见事件，提出高效稳定的风险估计方法。
Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
- 采用子采样与重加权策略降低计算开销。
- 在稀有结局下显著提升估计稳定性与收敛性。
- 适用于医疗大数据中的长期因果推断，如自杀风险研究。
在大规模观察性研究中，估计时变治疗对生存结局的因果效应计算成本高，尤其当结局罕见时更为突出。尽管基于g-formula的方法（如迭代条件期望，ICE）为纵向因果推断提供了合理框架，但其计算代价大，尤其是需要基于自助法进行方差估计时。此外，每个时间点的结果稀疏导致严重类别不平衡，使逻辑回归及相关模型出现不稳定和不收敛问题。为此，我们提出一种适用于纵向生存数据的系统性子采样与重加权策略，可应用于多种现有因果效应估计器（包括ICE）。该方法大幅降低计算负担，同时保持估计一致性，并在稀有结局场景中改善稳定性。通过模拟实验和基于大型电子健康记录队列的研究（聚焦社会行为健康决定因素与自杀风险），验证了该方法在建模纵向数据中罕见事件的有效性。
原文摘要 · Abstract (English)
Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly when outcomes are rare. While g-formula-based methods such as the iterative conditional expectation (ICE) estimator provide a principled framework for longitudinal causal inference, they become computationally expensive, especially when bootstrap-based variance estimation is required. In addition, outcome rarity at each time point induces severe class imbalance, leading to instability and convergence issues in logistic regression and related models. To address these challenges, we propose a principled subsampling and reweighting strategy for longitudinal survival data that can be applied to a range of existing causal effect estimators in this setting, including the ICE estimator. The proposed method substantially reduces computational burden while preserving consistency and improving estimation stability in rare-outcome settings. We evaluate the method through simulations and validate it using a large-scale EHR cohort study on social and behavioral determinants of health (SBDH) and suicide risk, demonstrating its effectiveness for modeling rare outcomes in longitudinal data.
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