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A note on overadjustment in inverse probability weighted estimation

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  • Andrea Rotnitzky
  • Lingling Li
  • Xiaochun Li

Abstract

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. Copyright 2010, Oxford University Press.

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Bibliographic Info

Article provided by Biometrika Trust in its journal Biometrika.

Volume (Year): 97 (2010)
Issue (Month): 4 ()
Pages: 997-1001

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Handle: RePEc:oup:biomet:v:97:y:2010:i:4:p:997-1001

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Cited by:
  1. Bartolucci, Francesco & Grilli, Leonardo & Pieroni, Luca, 2012. "Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator," MPRA Paper 43430, University Library of Munich, Germany.

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