找最优干预方案，排除中介路径影响，高效且可靠。
Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
- 基于因果带子框架，用可观测干预数据识别期望自然直接潜在结果
- 提出固定置信度最优臂识别算法，样本效率高且正确率有保证
- 适用于广告投放等需剔除中介效应的决策场景
本文研究在因果带子设置下，如何识别使期望自然直接潜在结果（NDPO）最大化的处理方案，该指标排除了研究者希望从评估中移除的中介路径影响。首先，我们利用可观测的干预分布建立了总体层面的NDPO识别性。随后，基于Track-and-Stop（TaS）框架，提出一种固定置信度最优臂识别（BAI）算法，采用割集法求解由此产生的半无限优化问题。所提算法在高概率意义下实现样本高效识别，并满足δ-正确性与渐近最优性。最后，通过大规模真实广告数据集IPinYou上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an intervention while excluding the pathway transmitted through a mediator that researchers may wish to remove from evaluation. We first establish population-level identification of the expected NDPO in a causal bandit setting using observable interventional distributions. We then develop a fixed-confidence best-arm identification (BAI) algorithm based on the Track-and-Stop (TaS) framework, employing a cutting-set method to solve the resulting semi-infinite optimization problem. The proposed algorithm achieves sample-efficient identification with a high-probability correctness guarantee. We prove that it satisfies $δ$-correctness and asymptotic optimality. Finally, we validate the approach through empirical evaluations on a large-scale real-world advertising dataset (IPinYou).
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