Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?

Corentin  Segalas; Clemence  Leyrat; Elizabeth  Williamson

doi:10.3329/jsr.v55i2.58806

Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?

Authors

Corentin Segalas Universit´e de Paris, Centre of Epidemiology and Statistics (CRESS) Inserm, F75004, Paris, France
Clemence Leyrat Department of Medical Statistics, London School of Hygiene and Tropical medicine, UK
Elizabeth Williamson Department of Medical Statistics, London School of Hygiene and Tropical medicine, UK

DOI:

https://doi.org/10.3329/jsr.v55i2.58806

Keywords:

Bias-correction; Bootstrap; Ignorability; Inverse Probability of Treatment Weighting; Propensity scores; Unconfoundedness

Abstract

Inverse probability of treatment weighting can account for confounding under a number of assumptions, including that of no unmeasured confounding. A recent simulation study proposed a bootstrap bias correction, apparently demonstrating good performance in removing bias due to unmeasured confounding. We revisited the simulations, finding no evidence of bias reduction.

Journal of Statistical Research 2021, Vol. 55, No. 2, pp. 293-297

Abstract
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Published

2022-03-30

How to Cite

Segalas, C. ., Leyrat, C. ., & Williamson, E. . (2022). Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?. Journal of Statistical Research, 55(2), 293–297. https://doi.org/10.3329/jsr.v55i2.58806