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Balancing Covariates via Propensity Score Weighting

Publication ,  Journal Article
Li, F; Morgan, KL; Zaslavsky, AM
Published in: Journal of the American Statistical Association
January 2, 2018

Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for balancing covariates. We define a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. General large-sample results on nonparametric estimation based on these weights are derived. We further propose a new weighting scheme, the overlap weights, in which each unit’s weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable small-sample exact balance property, based on which we propose a new method that achieves exact balance for means of any selected set of covariates. Two applications illustrate these methods and compare them with other approaches.

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Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2, 2018

Volume

113

Issue

521

Start / End Page

390 / 400

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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Li, F., Morgan, K. L., & Zaslavsky, A. M. (2018). Balancing Covariates via Propensity Score Weighting. Journal of the American Statistical Association, 113(521), 390–400. https://doi.org/10.1080/01621459.2016.1260466
Li, F., K. L. Morgan, and A. M. Zaslavsky. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113, no. 521 (January 2, 2018): 390–400. https://doi.org/10.1080/01621459.2016.1260466.
Li F, Morgan KL, Zaslavsky AM. Balancing Covariates via Propensity Score Weighting. Journal of the American Statistical Association. 2018 Jan 2;113(521):390–400.
Li, F., et al. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association, vol. 113, no. 521, Jan. 2018, pp. 390–400. Scopus, doi:10.1080/01621459.2016.1260466.
Li F, Morgan KL, Zaslavsky AM. Balancing Covariates via Propensity Score Weighting. Journal of the American Statistical Association. 2018 Jan 2;113(521):390–400.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2, 2018

Volume

113

Issue

521

Start / End Page

390 / 400

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics