### 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 |
---|---|

Pages (from-to) | 997-1001 |

Number of pages | 5 |

Journal | Biometrika |

Volume | 97 |

Issue number | 4 |

DOIs | |

State | Published - Dec 2010 |

### Fingerprint

### 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

### Cite this

*Biometrika*,

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

**A note on overadjustment in inverse probability weighted estimation.** / Rotnitzky, Andrea; Li, Lingling; Li, Xiaochun.

Research output: Contribution to journal › Article

*Biometrika*, vol. 97, no. 4, pp. 997-1001. https://doi.org/10.1093/biomet/asq049

}

TY - JOUR

T1 - A note on overadjustment in inverse probability weighted estimation

AU - Rotnitzky, Andrea

AU - Li, Lingling

AU - Li, Xiaochun

PY - 2010/12

Y1 - 2010/12

N2 - 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.

AB - 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.

KW - Causal inference

KW - Propensity score

KW - Standardized mean

UR - http://www.scopus.com/inward/record.url?scp=78651325910&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78651325910&partnerID=8YFLogxK

U2 - 10.1093/biomet/asq049

DO - 10.1093/biomet/asq049

M3 - Article

AN - SCOPUS:78651325910

VL - 97

SP - 997

EP - 1001

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 4

ER -