Stable weights that balance covariates for estimation with incomplete outcome data. 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.

References in zbMATH (referenced in 19 articles , 1 standard article )

Showing results 1 to 19 of 19.
Sorted by year (citations)

  1. Zhang, Min; Zhang, Baqun: A stable and more efficient doubly robust estimator (2022)
  2. Zhan, Ming-feng; Cai, Zong-wu; Fang, Ying; Lin, Ming: Recent advances in statistical methodologies in evaluating program for high-dimensional data (2022)
  3. Hirshberg, David A.; Wager, Stefan: Augmented minimax linear estimation (2021)
  4. Kuang, Kun; Li, Yunzhe; Li, Bo; Cui, Peng; Yang, Hongxia; Tao, Jianrong; Wu, Fei: Continuous treatment effect estimation via generative adversarial de-confounding (2021)
  5. Liangyuan Hu, Jiayi Ji: CIMTx: An R package for causal inference with multiple treatments using observational data (2021) arXiv
  6. Zhang, Xiaoke; Xue, Wu; Wang, Qiyue: Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies (2021)
  7. Bennett, Magdalena; Vielma, Juan Pablo; Zubizarreta, José R.: Building representative matched samples with multi-valued treatments in large observational studies (2020)
  8. Cheng, David; Ayyagari, Rajeev; Signorovitch, James: The statistical performance of matching-adjusted indirect comparisons: estimating treatment effects with aggregate external control data (2020)
  9. Hazlett, Chad: Kernel balancing: a flexible non-parametric weighting procedure for estimating causal effects (2020)
  10. Tianhui Zhou, Guangyu Tong, Fan Li, Laine E. Thomas, Fan Li: PSweight: An R Package for Propensity Score Weighting Analysis (2020) arXiv
  11. Yang, Shu; Kim, Jae Kwang: Statistical data integration in survey sampling: a review (2020)
  12. Antonelli, Joseph; Parmigiani, Giovanni; Dominici, Francesca: High-dimensional confounding adjustment using continuous Spike and Slab priors (2019)
  13. Li, Fan; Li, Fan: Propensity score weighting for causal inference with multiple treatments (2019)
  14. Zhao, Qingyuan: Covariate balancing propensity score by tailored loss functions (2019)
  15. Zhou, Tingting; Elliott, Michael R.; Little, Roderick J. A.: Penalized spline of propensity methods for treatment comparison (2019)
  16. Ding, Peng; Li, Fan: Causal inference: a missing data perspective (2018)
  17. Fong, Christian; Hazlett, Chad; Imai, Kosuke: Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements (2018)
  18. Santacatterina, Michele; Bottai, Matteo: Optimal probability weights for inference with constrained precision (2018)
  19. Zubizarreta, José R.: Stable weights that balance covariates for estimation with incomplete outcome data (2015)