ArticleBiometrics2017
Outcome-adaptive lasso: Variable selection for causal inference.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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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
70 citing papers in PubMed.
- A patient activation intervention in primary care for patients with chronic pain on long term opioid therapy: results from a randomized control trial.BMC health services research · 2024Trial
- Estimation of Covariate Balancing Propensity Score Based on Outcome Adaptive Lasso.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Outcome-Adaptive Lasso for Cox Proportional Hazards Model.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Variable selection-combined causal mediation analysis for continuous treatments with application to large-dimensional biomedical data.PLoS computational biology · 2026Article
- CausalKnowledgeTrace: A Novel Computational Framework for Automated Literature-Based Causal Graph Construction and Evidence-Based Variable Selection in Biomedical Research.bioRxiv : the preprint server for biology · 2026Article
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Altered temporal dynamics of prefrontal ERP responses reflecting neural adaptation in patients with amnestic mild cognitive impairment.Alzheimer's research & therapy · 2026Article
- Adaptive Sparsening and Smoothing of the Treatment Model for Longitudinal Causal Inference Using Outcome-Adaptive LASSO and Marginal Fused LASSO.Statistics in medicine · 2026Article
- Associations of adolescent menstrual symptoms with school absences and educational attainment: analysis of a prospective cohort study.NPJ science of learning · 2025Article
- Using overlap weights to address extreme propensity scores in estimating restricted mean counterfactual survival times.American journal of epidemiology · 2025Article
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 2025Article
- Modeling Versus Balancing Approaches to Addressing Instrumental Variables in Weighting: A Comparison of the Outcome-Adaptive Lasso, Stable Balancing Weighting, and Stable Confounder Selection.Pharmacoepidemiology and drug safety · 2025Article
- Estimating wage disparities using foundation models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.Pharmacoepidemiology and drug safety · 2025Article
- IJMPR Didactic Paper: Weighting for Causal Inference in Mental Health Research.International journal of methods in psychiatric research · 2025Article
- Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims.Statistical methods in medical research · 2025Article
- Home, but Homebound After Traumatic Brain Injury: Risk Factors and Associations With Nursing Home Entry and Death.Archives of physical medicine and rehabilitation · 2025Article
- Article
- Robust propensity score estimation via loss function calibration.Statistical methods in medical research · 2025Article
- A doubly robust estimator for continuous treatments in high dimensions.BMC medical research methodology · 2025Article
10 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a "throw in the kitchen sink" approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators. In particular, the inclusion of covariates that impact exposure but not the outcome, can inflate standard errors without improving bias, while the inclusion of covariates associated with the outcome but unrelated to exposure can improve precision. We propose the outcome-adaptive lasso for selecting appropriate covariates for inclusion in propensity score models to account for confounding bias and maintaining statistical efficiency. This proposed approach can perform variable selection in the presence of a large number of spurious covariates, that is, covariates unrelated to outcome or exposure. We present theoretical and simulation results indicating that the outcome-adaptive lasso selects the propensity score model that includes all true confounders and predictors of outcome, while excluding other covariates. We illustrate covariate selection using the outcome-adaptive lasso, including comparison to alternative approaches, using simulated data and in a survey of patients using opioid therapy to manage chronic pain.
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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.