### 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.

Original language | English (US) |
---|---|

Pages (from-to) | 997-1001 |

Number of pages | 5 |

Journal | Biometrika |

Volume | 97 |

Issue number | 4 |

DOIs | |

State | Published - Dec 2010 |

### Keywords

- Causal inference
- Propensity score
- Standardized mean

### ASJC Scopus subject areas

- Agricultural and Biological Sciences(all)
- Agricultural and Biological Sciences (miscellaneous)
- Statistics and Probability
- Mathematics(all)
- Applied Mathematics
- Statistics, Probability and Uncertainty

## Fingerprint Dive into the research topics of 'A note on overadjustment in inverse probability weighted estimation'. Together they form a unique fingerprint.

## Cite this

*Biometrika*,

*97*(4), 997-1001. https://doi.org/10.1093/biomet/asq049