Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 2, April 2024
Page(s) 117 - 124
DOI https://doi.org/10.1051/wujns/2024292117
Published online 14 May 2024
  1. Imbens G W, Angrist J D. Identification and estimation of local average treatment effects [J]. Econometrica, 1994, 62(2): 467-475. [CrossRef] [MathSciNet] [Google Scholar]
  2. Angrist J D, Imbens G W, Rubin D B. Identification of causal effects using instrumental variables [J]. Journal of the American Statistical Association, 1996, 91(434): 444-455. [CrossRef] [Google Scholar]
  3. Imbens G W. Nonparametric estimation of average treatment effects under exogeneity: A review [J]. Review of Economics and Statistics, 2004, 86(1): 4-29. [CrossRef] [Google Scholar]
  4. Rosenbaum P R, Rubin D B. The central role of the propensity score in observational studies for causal effects [J]. Biometrika, 1983, 70(1): 41-55. [CrossRef] [Google Scholar]
  5. Austin P C. An introduction to propensity score methods for reducing the effects of confounding in observational studies [J]. Multivariate Behavioral Research, 2011, 46(3): 399-424. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  6. Austin P C, Schuster T. The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study [J]. Statistical Methods in Medical Research, 2016, 25(5): 2214-2237. [Google Scholar]
  7. Herman M, Robins J M. Causal Inference: What if [M]. Boca Raton: Chapman & Hall/CRC, 2020. [Google Scholar]
  8. Imbens G W, Rubin D B. Causal Inference in Statistics, Social, and Biomedical Sciences [M]. Cambridge: Cambridge University Press, 2015. [Google Scholar]
  9. Lunceford J K, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study [J]. Statistics in Medicine, 2004, 23(19): 2937-2960. [Google Scholar]
  10. Cao Y X, Zhang C X, Yu C J. Estimating survival treatment effects with covariate adjustment using propensity score[J]. Acta Mathematica Sinica, English Series, 2022, 38(11): 2057-2068. [CrossRef] [MathSciNet] [Google Scholar]
  11. Cao Y X,Yu J C. Partial least squares method for treatment effect in observational studies with censored outcomes[J].Wuhan University Journal of Natural Sciences, 2018, 23(6): 487-492. [CrossRef] [MathSciNet] [Google Scholar]
  12. Lee M J. Simple least squares estimator for treatment effects using propensity score residuals [J]. Biometrika, 2018, 105(1): 149-164. [CrossRef] [MathSciNet] [Google Scholar]
  13. Rubin D B. Estimating causal effects of treatments in randomized and nonrandomized studies [J]. Journal of Educational Psychology, 1974, 66(5): 688. [CrossRef] [Google Scholar]
  14. Rubin D B. Inference and missing data [J]. Biometrika, 1976, 63(3): 581-592. [CrossRef] [MathSciNet] [Google Scholar]
  15. Holland P W. Statistics and causal inference [J]. Journal of the American Statistical Association, 1986, 81(396): 945-960. [CrossRef] [MathSciNet] [Google Scholar]
  16. Chen X. Large sample sieve estimation of semi-nonparametric models [J]. Handbook of Econometrics, 2007, 6: 5549-5632. [CrossRef] [Google Scholar]
  17. Dong C H, Gao J T, Peng B. Series estimation for single‐index models under constraints [J]. Australian & New Zealand Journal of Statistics, 2019, 61(3): 299-335. [CrossRef] [MathSciNet] [Google Scholar]
  18. Vansteelandt S, Daniel R M. On regression adjustment for the propensity score [J]. Statistics in Medicine, 2014, 33(23): 4053-4072. [Google Scholar]
  19. Zou B M, Zou F, Shuster J J, et al. On variance estimate for covariate adjustment by propensity score analysis [J]. Statistics in Medicine, 2016, 35(20): 3537-3548. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  20. Ministry of Education. Opinions on deepening the teaching reform of undergraduate education and comprehensively improving the quality of talent cultivation [EB/OL]. [2019-10-08]. http://www.moe.gov.cn/srcsite/A08/s7056/201910/t20191011_402759.html. [Google Scholar]
  21. The General Office of the CPC Central Committee, the State Council. Opinions on comprehensively strengthening and improving school sports work in the new era [EB/OL]. [2020-10-15]. http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/202010/t20201015_494794.html. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.