Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers
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通向同一答案的三条路径：协调 AIPW、TMLE 与双重机器学习
- 研究问题
- 在估计同一个平均处理效应时，实践者需要统一哪些辅助模型、交叉拟合与软件设置，才能解释三类估计结果的差异？
- 方法与思路
- 作者在公开戒烟与体重数据上共享 Super Learner 库和交叉拟合分组，手工实现三类方法，并与多个 R 软件包逐一协调。
- 材料陈述的贡献
- 摘要称匹配辅助模型和分组并平均分割噪声后，共享库的实现结果高度接近；教程强调先检查重叠性，再比较估计方法。
- 阅读关注点
- 应核对目标参数、共同影响函数、辅助模型训练与分组安排，以及无重叠例子如何区分不可识别问题和有限样本数值差异。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要将影响函数关系视为既有理论，单一应用的一致性不能保证任意软件或数据都一致。
原文摘要
Augmented inverse-probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three routes to the same efficient influence function for the average treatment effect --- settled theory we treat as background. This tutorial's contribution is its worked, shared-nuisance reconciliation on real data: what a practitioner must actually match for the routes, and the software packages, to agree. Working the effect of smoking cessation on weight change in the open NHEFS data (n=1566) with one shared Super Learner library and identical cross-fitting folds, we build all three estimators by hand from one influence function, in full-sample, cross-fit, and double-cross-fit variants; the six resulting doubly-robust estimates span only 3.32--3.42 kg, consistent with the established benchmark. We then reconcile the same estimand across our engine and the tmle, AIPW, DoubleML, and tmle3 packages. At their defaults the estimates span 3.32--3.49 kg. Once the library and folds are matched and single-split noise is averaged out, the three library-sharing implementations agree to within 0.01 kg --- so the residual spread traces to the nuisance library, folds, and repetitions, not to the estimator label. Because the estimators share one influence function, they agree under good overlap; under a positivity violation the pooled ATE is not identified without additional extrapolation assumptions, and their finite-sample estimates can then diverge sharply. We therefore place a positivity diagnosis ahead of estimator choice, illustrate the failure on a no-overlap example, and close with a reporting checklist. Open-source R code reproduces every number.