A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
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高维观察数据中稳健处理效应评估的两阶段特征选择
- 研究问题
- 在高维且协变量相关的医疗观察数据中，如何筛选适合匹配的变量，以改善处理效应估计的可靠性和计算效率？
- 方法与思路
- 作者提出结果自适应弹性网 OAENet 两阶段筛选方法，保留混杂变量及仅与结果相关的变量，再结合匹配开展模拟和医疗数据分析。
- 材料陈述的贡献
- 摘要称，OAENet 在模拟中改善估计质量或减少计算时间，并用于估计阿片类物质使用障碍与自杀行为之间的处理效应。
- 阅读关注点
- 应核对两阶段筛选与调参规则、匹配和效应目标、混杂控制假设，以及竞争方法是否同等调优和真实数据中的因果识别依据。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及接收状态未核实；摘要所称无偏与优越性缺少具体条件和数值，文献一致性不能验证因果识别。
原文摘要
A Randomized Control Trial (RCT) is considered as the gold standard for evaluating the effect of any intervention or treatment. However, its feasibility is often hindered by ethical, economical, and legal considerations, making observational data a valuable alternative for drawing causal conclusions. Nevertheless, healthcare observational data presents a difficult challenge due to its high dimensionality, requiring careful consideration to ensure unbiased, reliable, and robust causal inferences. To overcome this challenge, in this study, we propose a novel two-stage feature selection technique called, Outcome Adaptive Elastic Net (OAENet), explicitly designed for making robust causal inference decisions using matching techniques. OAENet offers several key advantages over existing methods: superior performance on correlated and high-dimensional data compared to the existing methods and the ability to select specific sets of variables (including confounders and variables associated only with the outcome). This ensures robustness and facilitates an unbiased estimate of the causal effect. Numerical experiments on simulated data demonstrate that OAENet significantly outperforms state-of-the-art methods by either producing a higher-quality estimate or a comparable estimate in significantly less time. To illustrate the applicability of OAENet, we employ large-scale US healthcare data to estimate the effect of Opioid Use Disorder (OUD) on suicidal behavior. When compared to competing methods, OAENet closely aligns with existing literature on the relationship between OUD and suicidal behavior. Performance on both simulated and real-world data highlights that OAENet notably enhances the accuracy of estimating treatment effects or evaluating policy decision-making with causal inference.