ArticleJournal of the American Statistical Association2021
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 56 citations in OpenAlex.
- A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making.Statistics in medicine · 2023Trial
- An application of weighted support vector machines to individualized second-line therapy selection after levodopa in Parkinson's disease.Frontiers in human neuroscience · 2026Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- A MARGINAL STRUCTURAL MODEL FOR PARTIAL COMPLIANCE IN SMARTS.The annals of applied statistics · 2024Article
- Article
- Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.Journal of the American Statistical Association · 2024Article
- Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.Statistical methods in medical research · 2023Article
- Instrumented difference-in-differences.Biometrics · 2023Article
- Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.Biometrical journal. Biometrische Zeitschrift · 2023Observational
- Accountable survival contrast-learning for optimal dynamic treatment regimes.Scientific reports · 2023Article
- Meta-analysis of individualized treatment rules via sign-coherency.Proceedings of machine learning research · 2022Article
- Rejoinder: Optimal individualized decision rules using instrumental variable methods.Journal of the American Statistical Association · 2021Article
- Machine intelligence for individualized decision making under a counterfactual world: A rejoinder.Journal of the American Statistical Association · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value function
Indexed as
Identifiers
What OpenQuestion holds
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.