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. 2023 Apr;32(4):691-711.
doi: 10.1177/09622802221146308. Epub 2023 Jan 24.

Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders

Grace Y Yi  1   2 , Li-Pang Chen  1   3
Affiliations

Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders

Grace Y Yi et al. Stat Methods Med Res. 2023 Apr.

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.

Keywords: Causal inference; inverse probability weight; measurement error; propensity score; simulation–extrapolation; variable selection.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

    1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41–55.
    1. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516–524.
    1. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med 2004; 23: 2937–2960. - PubMed
    1. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics 2005; 61: 962–973. - PubMed
    1. Westreich D, Cole SR, Funk MJ, et al.. The role of the c-statistic in variable selection for propensity score models. Pharmacoepidemiol Drug Saf 2011; 20: 317–320. - PMC - PubMed

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