Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers
Abstract
Augmented inverse probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three asymptotically equivalent routes to the same efficient influence function for the average treatment effect. We contribute a worked, shared-nuisance reconciliation on real data: what a practitioner must hold fixed for the routes and their software to agree, and when they do not. Using open data on smoking cessation and weight change (n = 1566), one Super Learner library, and folds matched within schemes, we build all three by hand, in full-sample, cross-fit, and double-cross-fit variants. A semi-synthetic benchmark with known sample-average effects on a fixed design grades interval coverage. The six doubly robust estimates span 3.32–3.39 kg over repeated splits. Single runs of our code and the tmle, AIPW, DoubleML, and tmle3 packages span 3.32–3.49 kg, compatible with fitting-scheme differences and single-split noise. Over 20 seeds, the three implementations sharing our library agree within 0.01 kg in median, although one is a TMLE. Agreement can fail: with in-sample nuisance fits on a binary mortality outcome, and under poor overlap in a job-training example. We recommend diagnosing overlap before choosing an estimator, and provide a reporting checklist and software code.