Balancing Covariates via Propensity Score Weighting

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 dis...

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Veröffentlicht in:Journal of the American Statistical Association Jg. 113; H. 521; S. 390 - 400
Hauptverfasser: Li, Fan, Morgan, Kari Lock, Zaslavsky, Alan M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 02.01.2018
Taylor & Francis Group,LLC
Taylor & Francis Ltd
Schlagworte:
Balancing
Balancing weights
Causal inference
Clinical equipoise
Confounding
Exact balance
Observational studies
Overlap weights
Probability
probability analysis
Propensity
Property
Regression analysis
Statistical methods
Statistics
Theory and Methods
variance
Weighting
Weighting methods
ISSN:0162-1459, 1537-274X, 1537-274X
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Zusammenfassung: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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ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2016.1260466