398
Views
0
CrossRef citations to date
0
Altmetric
Articles

Multiply robust estimation for average treatment effect among treated

&
Pages 29-39 | Received 06 Mar 2023, Accepted 10 Nov 2023, Published online: 15 Dec 2023

References

  • Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235–267. https://doi.org/10.1111/ecta.2006.74.issue-1
  • Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962–973. https://doi.org/10.1111/biom.2005.61.issue-4
  • Cao, W., Tsiatis, A. A., & Davidian, M. (2009). Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data. Biometrika, 96, 723–734. https://doi.org/10.1093/biomet/asp033
  • Chan, K. C. G., & Yam, S. C. P. (2014). Oracle, multiple robust and multipurpose calibration in a missing response problem. Statistical Science, 29(3), 380–396.
  • Chan, K. C. G., Yam, S. C. P., & Zhang, Z. (2016). Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. Journal of the Royal Statistical Society Series B: Statistical Methodology, 78(3), 673–700. https://doi.org/10.1111/rssb.12129
  • Chen, J., Sitter, R. R., & Wu, C. (2002). Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys. Biometrika, 89(1), 230–237. https://doi.org/10.1093/biomet/89.1.230
  • Chen, S., & Haziza, D. (2017). Multiply robust imputation procedures for the treatment of item nonresponse in surveys. Biometrika, 104(2), 439–453.
  • Deville, J., & Särndal, C.-E. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376–382. https://doi.org/10.1080/01621459.1992.10475217
  • Duan, X., & Yin, G. (2017). Ensemble approaches to estimating the population mean with missing response. Scandinavian Journal of Statistics, 44(4), 899–917. https://doi.org/10.1111/sjos.v44.4
  • Fan, J., Imai, K., Lee, I., Liu, H., Ning, Y., & Yang, X. (2023). Optimal covariate balancing conditions in propensity score estimation. Journal of Business and Economic Statistics, 41(1), 97–110. https://doi.org/10.1080/07350015.2021.2002159
  • Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25–46. https://doi.org/10.1093/pan/mpr025
  • Han, P. (2012). A note on improving the efficiency of inverse probability weighted estimator using the augmentation term. Statistics and Probability Letters, 82(12), 2221–2228. https://doi.org/10.1016/j.spl.2012.08.005
  • Han, P. (2014a). A further study of the multiply robust estimator in missing data analysis. Journal of Statistical Planning and Inference, 148, 101–110. https://doi.org/10.1016/j.jspi.2013.12.006
  • Han, P. (2014b). Multiply robust estimation in regression analysis with missing data. Journal of the American Statistical Association, 109(507), 1159–1173. https://doi.org/10.1080/01621459.2014.880058
  • Han, P. (2016a). Combining inverse probability weighting and multiple imputation to improve robustness of estimation. Scandinavian Journal of Statistics, 43(1), 246–260. https://doi.org/10.1111/sjos.v43.1
  • Han, P. (2016b). Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out. Biometrika, 103(3), 683–700. https://doi.org/10.1093/biomet/asw024
  • Han, P., Kong, L., Zhao, J., & Zhou, X. (2019). A general framework for quantile estimation with incomplete data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(2), 305–333. https://doi.org/10.1111/rssb.12309
  • Han, P., & Wang, L. (2013). Estimation with missing data: Beyond double robustness. Biometrika, 100(2), 417–430. https://doi.org/10.1093/biomet/ass087
  • Hernán, M. A., & Robins, J. M. (2018). Causal inference. Chapman & Hall/CRC.
  • Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161–1189. https://doi.org/10.1111/ecta.2003.71.issue-4
  • Imbens, G. W., & Rubin, D. B. (2015). Causal inference for statistics, social, and biomedical sciences: An introduction. Cambridge University Press.
  • Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness a comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523–539.
  • Kim, J. K. (2010). Calibration estimation using exponential tilting in sample surveys. Survey Methodology, 36(2), 145–155.
  • Kim, J. K., & Park, M. (2010). Calibration estimation in survey sampling. International Statistical Review, 78(1), 21–39. https://doi.org/10.1111/insr.2010.78.issue-1
  • Li, W., Yang, S., & Han, P. (2020). Robust estimation for moment condition models with data missing not at random. Journal of Statistical Planning and Inference, 207, 246–254. https://doi.org/10.1016/j.jspi.2020.01.001
  • Molina, J., Rotnitzky, A., Sued, M., & Robins, J. (2017). Multiple robustness in factorized likelihood models. Biometrika, 104, 561–581. https://doi.org/10.1093/biomet/asx027
  • Newey, W. K., & Smith, R. J. (2004). Higher order properties of GMM and generalized empirical likelihood estimators. Econometrica, 72(1), 219–255. https://doi.org/10.1111/ecta.2004.72.issue-1
  • Qin, J., & Lawless, J. (1994). Empirical likelihood and general estimating equations. The Annals of Statistics, 22(1), 300–325. https://doi.org/10.1214/aos/1176325370
  • Qin, J., Shao, J., & Zhang, B. (2008). Efficient and doubly robust imputation for covariate-dependent missing responses. Journal of the American Statistical Association, 103(482), 797–810. https://doi.org/10.1198/016214508000000238
  • Qin, J., & Zhang, B. (2007). Empirical-likelihood-based inference in missing response problems and its application in observational studies. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(1), 101–122. https://doi.org/10.1111/j.1467-9868.2007.00579.x
  • Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846–866. https://doi.org/10.1080/01621459.1994.10476818
  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33–38.
  • Rotnitzky, A., Lei, Q., Sued, M., & Robins, J. M. (2012). Improved double-robust estimation in missing data and causal inference models.. Biometrika, 99, 439–456. https://doi.org/10.1093/biomet/ass013
  • Schennach, S. (2007). Point estimation with exponentially tilted empirical likelihood. Annals of Statistics, 35(2), 634–672. https://doi.org/10.1214/009053606000001208
  • Shi, X., Miao, W., Nelson, J. C., & Tchetgen Tchetgen, E. (2020). Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(2), 521–540. https://doi.org/10.1111/rssb.12361
  • Tan, Z. (2010). Bounded, efficient and doubly robust estimation withinverse weighting. Biometrika, 97(3), 661–682. https://doi.org/10.1093/biomet/asq035
  • van der Laan, M. J., & Gruber, S. (2010). Collaborative double robust targeted maximum likelihood estimation. The International Journal of Biostatistics, 6(1), Article 17.
  • van der Vaart, A. W. (1998). Asymptotic statistics. Cambridge University Press.
  • Wang, L. (2019). Multiple robustness estimation in causal inference. Communications in Statistics - Theory and Methods, 48(23), 5701–5718. https://doi.org/10.1080/03610926.2018.1520881
  • Wang, L., & Tchetgen Tchetgen, E. (2018). Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(3), 531–550. https://doi.org/10.1111/rssb.12262
  • Wu, C., & Sitter, R. R. (2001). A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association, 96(453), 185–193. https://doi.org/10.1198/016214501750333054
  • Zhang, S., Han, P., & Wu, C. (2022). Calibration techniques encompassing survey sampling, missing data analysis and causal inference. International Statistical Review. https://doi.org/10.1111/insr.12518 .
  • Zhao, Q., & Percival, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1). https://doi.org/10.1515/jci-2016-0010