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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
(2002) · Cited by 13,211
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293
Introduction to Nonparametric Estimation
Tsybakov, A. (2008) · Springer Series in Statistics · doi:10.1007/b13794 · Cited by 3,687
The Central Role of the Propensity Score in Observational Studies for Causal Effects
Rosenbaum, P. R., & Rubin, D. B. (1983) · Biometrika · doi:10.2307/2335942 · Cited by 3,495
A Generalization of Sampling Without Replacement From a Finite Universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.2307/2280784 · Cited by 3,011
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,943
Constructing Inverse Probability Weights for Marginal Structural Models
Cole, S. R., & Hernan, M. A. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn164 · Cited by 2,796
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Primal-Dual Interior-Point Methods
Wright, S. J. (1997) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971453 · Cited by 2,409
Brucker, P. (1995) · Springer eBooks · doi:10.1007/978-3-662-03088-2_3 · Cited by 2,388
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Rubin, D. B. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001880 · Cited by 2,173
Conditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12027 · Cited by 1,379
Kang, J. D. Y., & Schafer, J. L. (2007) · Statistical Science · doi:10.1214/07-sts227 · Cited by 1,259
Normand, S. T., Landrum, M. B., Guadagnoli, E., Ayanian, J. Z., Ryan, T. J., Cleary, P. D., & McNeil, B. J. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00321-8 · Cited by 1,244
Design of Observational Studies
Rosenbaum, P. R. (2010) · Springer Series in Statistics · doi:10.1007/978-1-4419-1213-8 · Cited by 1,150
Hirano, K., & Imbens, G. W. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020371312283 · Cited by 1,039
Dealing with limited overlap in estimation of average treatment effects
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2009) · Biometrika · doi:10.1093/biomet/asn055 · Cited by 1,007
Rosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478441 · Cited by 998
Improving propensity score weighting using machine learning
Lee, B. K., Lessler, J., & Stuart, E. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3782 · Cited by 920
Forcing a sequential experiment to be balanced
EFRON, B. (1971) · Biometrika · doi:10.1093/biomet/58.3.403 · Cited by 873
For objective causal inference, design trumps analysis
Rubin, D. B. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas187 · Cited by 831
MacCoun, R. J. (2003) · Journal of Policy Analysis and Management · doi:10.1002/pam.10129 · Cited by 828
The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
Stuart, E. A., Cole, S. R., Bradshaw, C. P., & Leaf, P. J. (2011) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00673.x · Cited by 524
Kang, J., & Schafer, J. L. (2007) · Statistical Science · Cited by 368
Robins, J. M., Sued, M., Lei-Gomez, Q., Rotnitzky, A. G., Ciencias, F., & Naturales, E. (2010) · Cited by 346
Robins, J. M., Sued, M., Lei-Gomez, Q., & Rotnitzky, A. G. (2007) · Statistical Science · doi:10.1214/07-sts227d · Cited by 314
Bounded, efficient and doubly robust estimation with inverse weighting
Tan, Z. (2010) · Biometrika · doi:10.1093/biomet/asq035 · Cited by 305
Inference for imputation estimators
Robins, J. M. (2000) · Biometrika · doi:10.1093/biomet/87.1.113 · Cited by 278
Model Selection, Confounder Control, and Marginal Structural Models
Joffe, M. M., Have, T. R. T., Feldman, H. I., & Kimmel, S. E. (2004) · The American Statistician · doi:10.1198/000313004x5824 · Cited by 266
Fuller, W. A. (2009) · Wiley eBooks · doi:10.1002/9780470523551 · Cited by 237
Zubizarreta, J. R. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.703874 · Cited by 213
Applied Bayesian Modeling and Causal Inference from Incomplete‐Data Perspectives
Gelman, A., & Meng, X. (2004) · Wiley series in probability and statistics · doi:10.1002/0470090456 · Cited by 172
Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants
Baiocchi, M., Small, D. S., Lorch, S. A., & Rosenbaum, P. R. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2010.ap09490 · Cited by 166
Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
Miratrix, L. W., Sekhon, J. S., & Yu, B. (2012) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2012.01048.x · Cited by 156
Hogan, J. W., & Lancaster, T. (2004) · Statistical Methods in Medical Research · doi:10.1191/0962280204sm351ra · Cited by 154
Improved double-robust estimation in missing data and causal inference models
Rotnitzky, A. G., Lei, Q., Sued, M., & Robins, J. M. (2012) · Biometrika · doi:10.1093/biomet/ass013 · Cited by 152
Applied Bayesian Modeling And Causal Inference From Incomplete-Data Perspectives
Gelman, A., & Xiaoli, M. (2014) · Cited by 152
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: Rejoinder
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.2307/2669930 · Cited by 82
Effect of the 2010 Chilean Earthquake on Posttraumatic Stress
Zubizarreta, J. R., Cerdá, M., & Rosenbaum, P. R. (2012) · Epidemiology · doi:10.1097/ede.0b013e318277367e · Cited by 80
Yang, D., Small, D. S., Silber, J. H., & Rosenbaum, P. R. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01691.x · Cited by 75
A NEW PRIMAL-DUAL INTERIOR POINT METHOD FOR LINEAR OPTIMIZATION
Cho, G.-M. (2009) · Cited by 55
Zubizarreta, J. R., Small, D. S., Goyal, N. K., Lorch, S. A., & Rosenbaum, P. R. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas582 · Cited by 51
Zubizarreta, J. R., Reinke, C. E., Kelz, R. R., Silber, J. H., & Rosenbaum, P. R. (2011) · The American Statistician · doi:10.1198/tas.2011.11072 · Cited by 43
Model Averaging Methods for Weight Trimming.
Elliott, M. R. (2008) · PubMed · Cited by 29
Accounting for Limited Overlap in Estimation of Average Treatment Eects under Unconfoundedness
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2004) · Cited by 19
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473869 · Cited by 18
Research works printed but not linked: 2
- Elliott M.R., 2008, Journal of Official Statistics, 24, 517
- Rao J. N.K., 2009, Pakistan Journal of Statistics, 25, 371
Cited by
Sorted by citations
Synthetic Difference-in-Differences
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021) · American Economic Review · doi:10.1257/aer.20190159 · Cited by 1,518
The State of Applied Econometrics: Causality and Policy Evaluation
Athey, S. C., & Imbens, G. W. (2017) · The Journal of Economic Perspectives · doi:10.1257/jep.31.2.3 · Cited by 1,406
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163
Thomas (2020) · doi:10.1001/jama.2020.7819 · Cited by 835
The Augmented Synthetic Control Method
Ben-Michael, E., Feller, A., & Rothstein, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1929245 · Cited by 462
“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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
