Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers

arXiv:2609.26142 · stat.ME, stat.AP, stat.ML · Submitted 2026-08-08 · Read on arXiv

stat.ME, stat.AP, stat.ML

Submitted: 2026-08-08

Updated: 2026-08-08

Comments: 45 pages, 8 figures, 14 tables. Tutorial. R code at https://github.com/ehsanx/dml-alternatives, R package at https://github.com/ehsanx/dr3/

Code: https://github.com/ehsanx/dml-alternatives

Project page: https://ehsanx.github.io/dml-alternatives

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Causal Inference — 9× in this paper · explained in 4 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Exchangeability — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Infant Health and Development Program — 1× in this paper
• Influence Functions — 1× in this paper · explained in 2 episodes

Sources

• concrete: Targeted Estimation of Survival and Competing Risks in Continuous Time
• Semiparametric doubly robust targeted double machine learning: a review
• Cross-Fitting and Fast Remainder Rates for Semiparametric Estimation
• Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)

Related papers

• Doubly robust inference via calibration
• Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
• Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
• Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance
• A Survey on Archetypal Analysis
• Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States