{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.26142",
  "author_total": 1,
  "authors": [
    {
      "name": "M. Ehsan Karim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cyszkj3vmrt5g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.26142",
  "fulltext_url": null,
  "id": "cyszkj3vmrt5g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.26142",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7214126011",
  "pdf_url": "https://arxiv.org/pdf/2609.26142",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cyszkj3vmrt5g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:49Z",
      "native_id": "2609.26142",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.26142"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers",
  "title_text": "Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:10Z",
  "url": "https://research.draconitic.com/paper/cyszkj3vmrt5g",
  "venue": "arXiv",
  "versions": []
}