Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
大规模因果推断中核估计量的快速不确定性量化AI 解读 ↓
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大规模因果推断中核估计量的快速不确定性量化
- 研究问题
- 大样本中核方法反复拟合的成本使标准自助法难以实施时，如何降低治疗效应、政策评价和政策学习的不确定性量化成本？
- 方法与思路
- 作者将因果小包自助法扩展到核方法，结合子抽样与重抽样，并在核化策略学习、极小极大加权及核支持向量机双重机器学习中评估。
- 材料陈述的贡献
- 摘要称方法保留一阶不确定性量化和渐近正确覆盖，模拟中以较低成本获得名义覆盖区间，并用于出生体重效应及最优治疗方案分析。
- 阅读关注点
- 应核对子样本规模和重抽样次数的选择、覆盖保证的条件、三类实现的差异，以及出生数据应用中吸烟效应识别的依据。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未核实；摘要未列具体计算节省、覆盖误差或应用识别假设，不能推断任意核估计均适用。
原文摘要
Kernel methods are widely used in causal inference for tasks such as treatment effect estimation, policy evaluation, and policy learning. The bootstrap is a standard tool for uncertainty quantification because of its broad applicability. As increasingly large datasets become available, such as the 2023 U.S. Natality data from the National Vital Statistics System (NVSS), which includes 3,596,017 registered births, the computational demands of these methods increase substantially. Kernel methods are known to scale poorly with sample size, and this limitation is further exacerbated by the repeated re-fitting required by the bootstrap. As a result, bootstrap-based inference for kernel-based estimators can become computationally infeasible in large-scale settings. In this paper, we address these challenges by extending the causal Bag of Little Bootstraps (cBLB) algorithm to kernel methods. Our approach achieves computational scalability by combining subsampling and resampling while preserving first-order uncertainty quantification and asymptotically correct coverage. We evaluate the method across three representative implementations: kernelized augmented outcome-weighted learning, kernel-based minimax weighting, and double machine learning with kernel support vector machines. We show in simulations that our method yields confidence intervals with nominal coverage at a fraction of the computational cost. We further demonstrate its utility in a real-world application by estimating the effect of any amount of smoking on birth weight, as well as the optimal treatment regime, using the NVSS dataset, where the standard bootstrap is prohibitively expensive computationally and effectively infeasible at this scale.