Online Causal Inference with Application to Near Real-Time Post-Market Vaccine Safety Surveillance
在线因果推断及其在近实时疫苗上市后安全监测中的应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究流式观察数据中的处理效应在线估计、因果推断及干预安全监测，因此最直接对应因果推断。
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在线因果推断及其在近实时疫苗上市后安全监测中的应用
- 研究问题
- 面对连续到达的数据和多中心共享限制，如何在不保存或重新访问个体原始数据的情况下，持续更新处理效应估计与推断？
- 方法与思路
- 作者提出按数据批次递增更新的在线因果推断框架，推导一致性和渐近正态性，并通过模拟、R 软件包与疫苗安全监测应用说明。
- 材料陈述的贡献
- 摘要称，即使单个批次存在抽样偏差，只要合并数据是目标总体的随机样本，在线估计量仍渐近无偏；软件计算效率也得到展示。
- 阅读关注点
- 核对在线更新保留哪些统计信息、因果识别和渐近理论所需条件，以及合并样本代表性的要求如何与批次偏差相容。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；批次偏差稳健性带有合并样本条件，不能据此认定方法消除了混杂偏差。
原文摘要
Streaming data routinely generated by mobile phones, social networks, e-commerce, and electronic health records present new opportunities for near real-time surveillance of the impact of an intervention on an outcome of interest via causal inference methods. However, as data grow rapidly in volume and velocity, storing and combing data become increasingly challenging. The amount of time and effort spent to update analyses can grow exponentially, which defeats the purpose of instantaneous surveillance. Data sharing barriers in multi-center studies bring additional challenges to rapid signal detection and update. It is thus time to turn static causal inference to online causal learning that can incorporate new information as it becomes available without revisiting prior observations. In this paper, we present a framework for online estimation and inference of treatment effects leveraging a series of datasets that arrive sequentially without storing or re-accessing individual-level raw data. We establish estimation consistency and asymptotic normality of the proposed framework for online causal inference. In particular, our framework is robust to biased data batches in the sense that the proposed online estimator is asymptotically unbiased as long as the pooled data is a random sample of the target population regardless of whether each data batch is. We also provide an R package for analyzing streaming observational data that enjoys great computation efficiency compared to existing software packages for offline analyses. Our proposed methods are illustrated with extensive simulations and an application to sequential monitoring of adverse events post COVID-19 vaccine.