ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2022
Selective inference for effect modification via the lasso.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed.
- Effect of voucher receipt on perceived discrimination in boys and girls in the Moving to Opportunity study.American journal of epidemiology · 2026Trial
- An overview of modern machine learning methods for effect measure modification analyses in high-dimensional settings.SSM - population health · 2025Article
- Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes.Biometrics · 2025Article
- Minimax rates for heterogeneous causal effect estimation.Annals of statistics · 2024Article
- Predictors of teen sexual behavior.Children and youth services review · 2024Article
- Utilising causal inference methods to estimate effects and strategise interventions in observational health data.PloS one · 2024Article
- A reference-free R-learner for treatment recommendation.Statistical methods in medical research · 2023Article
- A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.Statistica Sinica · 2021Article
- Learning Optimal Distributionally Robust Individualized Treatment Rules.Journal of the American Statistical Association · 2021Article
- Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the lasso to choose a low-complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method.
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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.