Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?
Corentin Segalas;
Clemence Leyrat
;
Elizabeth Williamson;
(2022)
Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?
Journal of Statistical Research, 55 (2).
pp. 293-297.
ISSN 0256-422X
DOI: 10.3329/jsr.v55i2.58806
<jats:p>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</jats:p>
Item Type | Article |
---|---|
Elements ID | 176004 |
ORCID: https://orcid.org/0000-0002-4097-4577