ArticleBiometrics2025
Variable importance measures for heterogeneous treatment effects.
Article in Biometrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Causal effect on the number of life years lost due to a specific event: average treatment effect and variable importance.Lifetime data analysis · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests.Empirical economics · 2026Article
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.The annals of applied statistics · 2025Article
- Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.Statistical methods in medical research · 2025Article
- "Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift.Proceedings of machine learning research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Motivated by applications in precision medicine and treatment effect heterogeneity, recent research has focused on estimating conditional average treatment effects (CATEs) using machine learning (ML). CATE estimates may represent complicated functions that provide little insight into the key drivers of heterogeneity. Therefore, we introduce nonparametric treatment effect variable importance measures (TE-VIMs), based on the mean-squared error (MSE) in predicting the individual treatment effect. More precisely, TE-VIMs represent the increase in MSE when variables are removed from the CATE conditioning set. We derive efficient TE-VIM estimators which can be used with any CATE estimation strategy and are amenable to ML estimation. We propose several strategies to calculate these VIMs (eg, leave-one out, or keep-one in), using popular meta-learners for the CATE. We study the finite sample performance through a simulation study and illustrate their application using clinical trial data.
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.