The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
Abstract
Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of adequately adjusting for observed covariates in some settings. To address these limitations, this article presents a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher. This method allows the researcher to balance very precisely the means of the observed covariates and other features of their marginal and joint distributions, such as variances and correlations and also, for example, the quantiles of interactions of pairs and triples of observed covariates, thus, balancing entire two- and three-way marginals. Since the weighting method is based on a well-defined convex optimization problem, duality theory provides insight into the behavior of the variance of the optimal weights in relation to the level of covariate balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights? Also, the weighting method runs in polynomial time so relatively large datasets can be handled quickly. An implementation of the method is provided in the new package sbw for R. This article shows some theoretical properties of the resulting weights and illustrates their use by analyzing both a dataset from the 2010 Chilean earthquake and a simulated example.
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“modern statistical methods applied to large electronic health record databases to adjust for (“balance”) baseline differences in patients across types of treatment can be used to estimate aggregate treatment effects (131, 132).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [132]
Citations
Cites
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The Augmented Synthetic Control Method
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“een treated and control units, rather than balancing them indirectly by rst estimating the propensity score coecients. 3 Examples includeHainmueller(2011),Graham et al.(2012),Imai and Ratkovic(2013),Zubizarreta (2015),Tan(2017), andWang and Zubizarreta(2018). Several also combine weighting and outcome modeling, includ…”
Result
Metadata
- Authors
- Zubizarreta, J. R.
- Affiliations
- Columbia University (US)
- Corresponding author
- Zubizarreta, José R.
- ORCID
- Zubizarreta, José R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2015-07-03
- Volume
- 110
- Issue
- 511
- Pages
- 910-922
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2120817734
- Semantic Scholar
- 2425738e5b67fc909c6c41d7609e9d57e6791eb7
- Cited by
- 369
- References
- 54
- Citations by year
- 2026: 21 · 2025: 53 · 2024: 31 · 2023: 47 · 2022: 41 · 2021: 52 · 2020: 44 · 2019: 31 · 2018: 23 · 2017: 18 · 2016: 8
- FWCI
- 14.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- covariate balance, inverse probability weighting, convex optimization, duality theory, treatment effect estimation, causal inference
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