ArticleStatistical methods in medical research2023
Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders.
Article in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.Statistics in biosciences · 2024Article
- Estimating generalized propensity scores with survey and attrition weighted data.Statistics in medicine · 2024Article
- AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders.PloS one · 2024Article
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Authors and funding
2 authors.
Funding
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Abstract
In the framework of causal inference, the inverse probability weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement error. Ignoring these features and naively applying the usual inverse probability weighting estimation procedures may typically yield biased inference results. In this article, we develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.
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