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Biometrika. 2010 December; 97(4): 997–1001.
Published online 2010 July 31. doi:  10.1093/biomet/asq049
PMCID: PMC3371719

A note on overadjustment in inverse probability weighted estimation

Andrea Rotnitzky
Di Tella University, Sáenz Valiente 1010, Buenos Aires, Argentina, ; andrea/at/hsph.harvard.edu
Lingling Li
Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts 02115, U.S.A., lingling_li/at/post.harvard.edu

Summary

Standardized means, commonly used in observational studies in epidemiology to adjust for potential confounders, are equal to inverse probability weighted means with inverse weights equal to the empirical propensity scores. More refined standardization corresponds with empirical propensity scores computed under more flexible models. Unnecessary standardization induces efficiency loss. However, according to the theory of inverse probability weighted estimation, propensity scores estimated under more flexible models induce improvement in the precision of inverse probability weighted means. This apparent contradiction is clarified by explicitly stating the assumptions under which the improvement in precision is attained.

Some key words: Causal inference, Propensity score, Standardized mean

Articles from Biometrika are provided here courtesy of Oxford University Press