ArticleScandinavian journal of statistics, theory and applications2023
Multiply robust matching estimators of average and quantile treatment effects.
Article in Scandinavian journal of statistics, theory and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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
6 citing papers in PubMed.
- COADVISE: covariate adjustment with variable selection in randomized controlled trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Sensitivity analyses in longitudinal clinical trials via distributional imputation.Statistical methods in medical research · 2023Article
- Nearest neighbor ratio imputation with incomplete multinomial outcome in survey sampling.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Utilizing stratified generalized propensity score matching to approximate blocked randomized designs with multiple treatment levels.Journal of biopharmaceutical statistics · 2022Article
- Practical recommendations on double score matching for estimating causal effects.Statistics in medicine · 2022Observational
- RWD-INTEGRATED RANDOMIZED CLINICAL TRIAL ANALYSIS.Biopharmaceutical report · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however requiring stringent model assumptions. In this article, we propose novel double score matching (DSM) utilizing both the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the de-biasing DSM estimator achieves the multiple robustness property in that it is consistent if any one of the score models is correctly specified. We characterize the asymptotic distribution for the DSM estimator requiring only one correct model specification based on the martingale representations of the matching estimators and theory for local Normal experiments. We also provide a two-stage replication method for variance estimation and extend DSM for quantile estimation. Simulation demonstrates DSM outperforms single score matching and prevailing multiply robust weighting estimators in the presence of extreme propensity scores.
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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.