ArticleAmerican journal of epidemiology2021
AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 57 citations in OpenAlex.
- Individualized transfusion decisions to minimize adverse cardiovascular outcomes in patients with acute myocardial infarction and anemia.American heart journal · 2025Trial
- Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study.BMC medical research methodology · 2023Trial
- Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial.JAMA network open · 2022Trial
- The Comparative Effectiveness of Four Myopia-Control Spectacle Lenses: A Real-World AIPW Cohort Study.Healthcare (Basel, Switzerland) · 2026Article
- Oral Nicotine Product Use and E-Cigarette Use Persistence in Adolescents and Young Adults: An Analysis Using Augmented Inverse Probability Weighting.American journal of epidemiology · 2026Article
- Effects of Psychiatric Inpatient or Residential Care on Service Use After Psychiatric Emergency Department Boarding.Psychiatric services (Washington, D.C.) · 2026Article
- Electrographic Features of Catatonia With or Without Comorbid Delirium.The Journal of neuropsychiatry and clinical neurosciences · 2026Article
- Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).American journal of epidemiology · 2025Article
- Performance of Cross-Validated Targeted Maximum Likelihood Estimation.Statistics in medicine · 2025Article
- Intravenous Thrombolysis in Patients Taking Direct Oral Anticoagulation Treatment Before Stroke Onset: Results from the Safe Implementations of Treatments in Stroke International Stroke Registry.Annals of neurology · 2025Article
- Inpatient Hospitalizations for COVID-19 Among Patients With Prader-Willi Syndrome: A National Inpatient Sample Analysis.American journal of medical genetics. Part A · 2025Article
- Article
- A new four-arm within-study comparison: Design, implementation, and data.Observational studies · 2025Article
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Inpatient Hospitalizations for COVID-19 Among Patients with Prader-Willi Syndrome: a National Inpatient Sample Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024Article
- Molecular mechanisms of artificial light at night affecting circadian rhythm disturbance.Archives of toxicology · 2024Review
- Article
- Implementing TMLE in the presence of a continuous outcome.Research methods in medicine & health sciences · 2024Article
- Optimal adjustment sets for causal query estimation in partially observed biomolecular networks.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM, npcausal, tmle, and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.