When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition
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TMLE 与 AIPW 中样本分割选择何时重要：交叉拟合、目标化与重复
- 研究问题
- 摘要称，研究在二元处理平均处理效应的已知真值模拟中，TMLE 与 AIPW 的不同样本分割与目标化构造（谁训练 nuisance、如何纠偏、是否重复分割）是否会导致覆盖率的实质差异。
- 方法与思路
- 作者提出在已知真值模拟中比较八种主要估计量，各设定共享同一 nuisance 库、内层折与截断界，并以预设的两个百分点边际判断覆盖率差异；另用两个真实数据分析展示敏感性。
- 材料陈述的贡献
- 摘要称，结果显示当经验过程条件难以论证时应交叉拟合两个 nuisance；在重叠较差时应评估截断与 AIPW 对 TMLE 的敏感性；重复分割降低划分依赖但使部分区间更保守。
- 阅读关注点
- 应核对模拟设定与预设边际的具体定义、八种估计量的构造细节、近正性下 AIPW 优于 TMLE 的条件，以及真实数据分析是否支持模拟结论。
- 阅读边界
- 摘要未说明识别假设、定理、估计量渐近性质与真实数据的具体来源；且这是 arXiv 预印本，不能据此声称已同行评审或期刊接收。
原文摘要
Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias is corrected, and whether splits are repeated. In known-truth simulations of a binary-outcome average treatment effect, eight headline estimators shared each setting's library, inner folds and truncation bound; coverage differences were judged against a prespecified two-percentage-point margin. With a low-complexity library and good overlap (n = 3000), all but a half-sample baseline covered equivalently (0.928-0.939). With one random forest on the primary data-generating mechanism, estimators fitting the propensity in-sample undercovered severely (0.12-0.48), whereas cross-fitting both nuisances raised coverage to 0.91-1.00, two overcovering. Under near-positivity, every AIPW estimator covered better than every TMLE estimator with the unweighted fluctuation (0.907-0.917 versus 0.656-0.806 at the primary setting, all undercovering); looser truncation widened the gap. At the primary settings, pooled-versus-fold-specific and single-versus-double cross-fit constructions (involving four additional estimators) were usually equivalent; under extreme weights, pooled-targeting estimates were 1.7-1.8 times as dispersed as fold-specific ones. Repetition reduced partition dependence but made several intervals more conservative. Two real-data analyses illustrate these sensitivities. Results support cross-fitting both nuisances when empirical-process conditions cannot be justified, and assessing truncation and AIPW-versus-TMLE sensitivity under poor overlap.