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Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

Citations

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Cited by:

  1. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
  2. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
  3. Zhongzhe Ouyang & Lu Wang & Alzheimer’s Disease Neuroimaging Initiative, 2024. "Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies," Mathematics, MDPI, vol. 12(7), pages 1-14, March.
  4. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  5. Jiang, Qingshan & Xu, Li & Huang, Can, 2022. "Covariates distributions balancing for continuous treatment," Economics Letters, Elsevier, vol. 217(C).
  6. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
  7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  8. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
  9. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
  10. Zongwu Cai & Ying Fang & Ming Lin & Yaqian Wu, 2024. "Estimating Counterfactual Distribution Functions via Optimal Distribution Balancing with Applications," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202415, University of Kansas, Department of Economics.
  11. Cantoni, Eva & de Luna, Xavier, 2020. "Semiparametric inference with missing data: Robustness to outliers and model misspecification," Econometrics and Statistics, Elsevier, vol. 16(C), pages 108-120.
  12. Taisuke Otsu & Chen Qiu, 2018. "Information theoretic approach to high dimensional multiplicative models: Stochastic discount factor and treatment effect," STICERD - Econometrics Paper Series 595, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  13. Yu, Lamont Bo & Tran, Trang My & Lee, Wang-Sheng, 2023. "Bridging the gap: Assessing the effects of railway infrastructure investments in Northwest China," China Economic Review, Elsevier, vol. 82(C).
  14. Md Saiful Islam & Md Sarowar Morshed & Md. Noor-E-Alam, 2022. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3023-3041, November.
  15. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
  16. Isaac Meza, 2025. "Residual Balancing for Non-Linear Outcome Models in High Dimensions," Papers 2511.00324, arXiv.org.
  17. Mingfeng Zhan & Zongwu Cai & Ying Fang & Ming Lin, 2020. "Covariate Balance Weighting Methods in Estimating Treatment Effects: An Empirical Comparison," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202020, University of Kansas, Department of Economics, revised Dec 2020.
  18. Kentaro Kawato, 2025. "Balancing Weights for Causal Mediation Analysis," Papers 2512.09337, arXiv.org.
  19. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
  20. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
  21. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
  22. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
  23. Zezhen (Dawn) He & Vithala R. Rao, 2024. "Methods for Causal Inference in Marketing," Foundations and Trends(R) in Marketing, now publishers, vol. 18(3-4), pages 176-309, July.
  24. Chen, Shanting & Mallory, Allen B., 2021. "The effect of racial discrimination on mental and physical health: A propensity score weighting approach," Social Science & Medicine, Elsevier, vol. 285(C).
  25. Guilherme W F Barros & Marie Eriksson & Jenny Häggström, 2023. "Performance of modeling and balancing approach methods when using weights to estimate treatment effects in observational time-to-event settings," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-19, December.
  26. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
  27. Guido Imbens & Yiqing Xu, 2024. "Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?," Papers 2406.00827, arXiv.org, revised May 2025.
  28. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
  29. Yimin Dai & Ying Yan, 2024. "Mahalanobis balancing: A multivariate perspective on approximate covariate balancing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(4), pages 1450-1471, December.
  30. Benjamin Lu & Eli Ben-Michael & Avi Feller & Luke Miratrix, 2023. "Is It Who You Are or Where You Are? Accounting for Compositional Differences in Cross-Site Treatment Effect Variation," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 420-453, August.
  31. repec:kan:wpaper:202315 is not listed on IDEAS
  32. Md Saiful Islam & Md Sarowar Morshed & Gary J Young & Md Noor-E-Alam, 2019. "Robust policy evaluation from large-scale observational studies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
  33. Sean Yiu & Li Su, 2018. "Covariate association eliminating weights: a unified weighting framework for causal effect estimation," Biometrika, Biometrika Trust, vol. 105(3), pages 709-722.
  34. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
  35. Lee, Seong-ho & Ma, Yanyuan & de Luna, Xavier, 2025. "Covariate balancing for causal inference on categorical and continuous treatments," Econometrics and Statistics, Elsevier, vol. 33(C), pages 304-329.
  36. Bo Yuan & Shulei Wang, 2025. "Microbiome data integration via shared dictionary learning," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
  37. Vahe Avagyan & Stijn Vansteelandt, 2021. "Stable inverse probability weighting estimation for longitudinal studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1046-1067, September.
  38. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
  39. Qiu, Chen & Otsu, Taisuke, 2022. "Information theoretic approach to high dimensional multiplicative models: stochastic discount factor and treatment effect," LSE Research Online Documents on Economics 110494, London School of Economics and Political Science, LSE Library.
  40. Sean Yiu & Li Su, 2022. "Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models," Biometrics, The International Biometric Society, vol. 78(1), pages 115-127, March.
  41. Michael Zimmert, 2018. "The Finite Sample Performance of Treatment Effects Estimators based on the Lasso," Papers 1805.05067, arXiv.org.
  42. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
  43. Peter H. Egger & Filip Tarlea, 2021. "Comparing Apples to Apples: Estimating Consistent Partial Effects of Preferential Economic Integration Agreements," Economica, London School of Economics and Political Science, vol. 88(350), pages 456-473, April.
  44. Michael C. Knaus, 2024. "Treatment Effect Estimators as Weighted Outcomes," Papers 2411.11559, arXiv.org, revised Dec 2024.
  45. Furno, Marilena, 2021. "The synthetic control approach: Multivalued treatments at the quantiles," Research in Economics, Elsevier, vol. 75(1), pages 7-20.
  46. Dasom Lee & Shu Yang & Lin Dong & Xiaofei Wang & Donglin Zeng & Jianwen Cai, 2023. "Improving trial generalizability using observational studies," Biometrics, The International Biometric Society, vol. 79(2), pages 1213-1225, June.
  47. Kevin P. Josey & Elizabeth Juarez‐Colunga & Fan Yang & Debashis Ghosh, 2021. "A framework for covariate balance using Bregman distances," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 790-816, September.
  48. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "Doubly Robust Identification for Causal Panel Data Models," Papers 1909.09412, arXiv.org, revised Feb 2022.
  49. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
  50. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
  51. Adusumilli, Karun & Otsu, Taisuke & Qiu, Chen, 2023. "Reweighted nonparametric likelihood inference for linear functionals," LSE Research Online Documents on Economics 120198, London School of Economics and Political Science, LSE Library.
  52. Kallus Nathan & Santacatterina Michele, 2021. "Optimal balancing of time-dependent confounders for marginal structural models," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 345-369, January.
  53. Herrera, Diego & Cunniff, Shannon & DuPont, Carolyn & Cohen, Benjamin & Gangi, Dakota & Kar, Devyani & Peyronnin Snider, Natalie & Rojas, Victor & Wyerman, Jim & Norriss, Jessie & Mountenot, Marshall, 2019. "Designing an environmental impact bond for wetland restoration in Louisiana," Ecosystem Services, Elsevier, vol. 35(C), pages 260-276.
  54. María de los Angeles Resa & José R. Zubizarreta, 2020. "Direct and stable weight adjustment in non‐experimental studies with multivalued treatments: analysis of the effect of an earthquake on post‐traumatic stress," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1387-1410, October.
  55. Parast Layla & Hunt Priscillia & Griffin Beth Ann & Powell David, 2020. "When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 209-228, January.
  56. Susan Athey & Guido Imbens & Zhaonan Qu & Davide Viviano, 2025. "Triply Robust Panel Estimators," Papers 2508.21536, arXiv.org, revised Feb 2026.
  57. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Feb 2026.
  58. Wu Xue & Xiaoke Zhang & Kwun Chuen Gary Chan & Raymond K. W. Wong, 2024. "RKHS-based covariate balancing for survival causal effect estimation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 34-58, January.
  59. Zhang, Xiaoke & Xue, Wu & Wang, Qiyue, 2021. "Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
  60. Chen Qiu & Taisuke Otsu, 2022. "Information theoretic approach to high‐dimensional multiplicative models: Stochastic discount factor and treatment effect," Quantitative Economics, Econometric Society, vol. 13(1), pages 63-94, January.
  61. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
  62. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
  63. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
  64. Phillip Heiler, 2022. "Efficient Covariate Balancing for the Local Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1569-1582, October.
  65. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
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