ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2018
Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Effect of colonoscopy screening on risks of colorectal cancer and related death: instrumental variable estimation of per-protocol effects.European journal of epidemiology · 2025Trial
- An application of weighted support vector machines to individualized second-line therapy selection after levodopa in Parkinson's disease.Frontiers in human neuroscience · 2026Article
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
- Effectiveness of slow-release oral morphine versus other OAT regimens in key sub-populations: protocol for population-based target trial emulation.medRxiv : the preprint server for health sciences · 2025Article
- Covariate-assisted bounds on causal effects with instrumental variables.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Instrumental variable approaches for estimating time-varying treatment effects in comparative effectiveness research.BMC medical research methodology · 2025Article
- Using negative controls to identify causal effects with invalid instrumental variables.Biometrika · 2025Article
- On the asymptotic validity of confidence sets for linear functionals of solutions to integral equations.Biometrika · 2025Article
- Doubly robust machine learning-based estimation methods for instrumental variables with an application to surgical care for cholecystitis.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Statistical inference for time-to-event data in non-randomized cohorts with selective attrition.Statistics in medicine · 2024Article
- Multiply robust estimators in longitudinal studies with missing data under control-based imputation.Biometrics · 2024Article
- Generalizing the intention-to-treat effect of an active control from historical placebo-controlled trials: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study.Journal of the American Statistical Association · 2024Article
- Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.Journal of the American Statistical Association · 2024Article
- Group sequential testing under instrumented difference-in-differences approach.Statistics in medicine · 2023Article
- Instrumented difference-in-differences.Biometrics · 2023Article
- Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.BMC medical research methodology · 2023Review
- Instrumental variable estimation of the marginal structural Cox model for time-varying treatments.Biometrika · 2023Article
- Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure.Statistics in medicine · 2023Article
- Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.Statistics surveys · 2023Article
- Profiling compliers and noncompliers for instrumental variable analysis with covariates: A weighting approach.PloS one · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Instrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation, so that researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three different observed data models, and multiply robust estimators that are consistent in the union of these observed data models. We pay special attention to the case of binary outcomes, for which we obtain bounded estimators of the ATE that are guaranteed to lie between -1 and 1. Our approaches are illustrated with simulations and a data analysis evaluating the causal effect of education on earnings.
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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.