المؤلفون: 徐偉鑫, Hsu, Wei-Hsin

المساهمون: 陳立榜, Chen, Li-Pang

مصطلحات موضوعية: 平均處理效應, 因果推論, 共線性, 特徵篩選, 誤差校正, 超高維 度的協變量, ATE, Causal Inference, Collinearity, Feature Screening, Measurement Error Correction, Ultrahigh-Dimension

وصف الملف: 644846 bytes; application/pdf

Relation: Acerenza, S., Ban, K., and K ́edagni, D. (2022). Marginal treatment effects with misclassified treatment. arXiv:2105.00358\n\nBald ́e, I., Yang, Y.A., and Lefebvre, G. (2022). Reader reaction to “Outcome-adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017). Biometrics, 00, 1-7. https://doi.org/10.1111/biom.13683\n\nBang, H. and Robins, J.M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61, 962-973.\n\nBattistin, E. and Sianesi, B. (2011). Misclassified treatment status and treatment effects: an application to returns to education in the united kingdom. The Review of Economics and Statistics, 93, 495-509.\n\nBraun, D., Gorfine, M., Parmigiani, G., Arvold, N.D., Dominici, F., and Zigler, C. (2017). Propensity scores with misclassified treatment assignment: a likelihood-based adjustment. Biostatistics, 18, 695-710.\n\nChatterjee, S. (2021). A new coefficient of correlation. Journal of the American Statistical Association, 116, 2009-2022.\n\nChen, L.-P. (2020). Causal inference for left-truncated and right-censored data with covariates measurement error. Computational & Applied Mathematics, 39:126. DOI:10.1007/s40314-020-01152-4.\n\nChen, L.-P. and Yi, G.Y. (2021a). Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error. Annals of the Institute of Statistical Mathematics, 73, 481-517.\n\nChen, L.-P. and Yi, G.Y. (2021b). Analysis of noisy survival data with graphical proportional hazards measurement error models. Biometrics, 77, 956-969.\n\nChen, L.-P. (2023). A note of feature screening via rank-based coefficient of correlation. Biometrics Journal, To appear. DOI:10.1002/bimj.202100373.\n\nErtefaie, A., Asgharian, M., and Stephens, D.A. (2018). Variable selection in causal inference using a simultaneous penalization method. Journal Causal Inference, 6: 20170010, 1-16.\n\nGhosh, D., Zhu, Y., and Coffman, D.L. (2015). Penalized regression procedures for variable selection in the potential outcomes framework. Statistics in Medicine, 34, 1645-1658.\n\nHern ́an, M.A. and Robins, J.M. (2020). Causal Inference: What If. Chapman & Hall/CRC, Boca Raton. Link: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/\n\nKoch, B., Vock, D.M., and Wolfson, J. (2020). Variable selection and estimation in causal inference using Bayesian spike and slab priors. Statistical Methods in Medical Research, 29, 2445-2469.\n\nKyle, R.P., Moodie, E.E.M., and Klein, M.B. (2016). Correcting for measurement error in time-varying covariates in marginal structural models. American Journal Epidemiology, 184, 249-258.\n\nLewbel, A. (2007). Endogenous selection or treatment model estimation.Journal of Econometrics, 141, 777-806.\n\nLunceford, J.K. and Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects. a comparative study. Statistics in Medicine, 23, 2937-2960.\n\nMcCaffrey, D.F., Lockwood, J.R., and Setodji, C.M. (2013). Inverse probability weighting with error-prone covariates. Biometrika, 100, 671-680.\n\nNegi, A. and Negi, D.S. (2022). Difference-in-differences with a misclassified treatment. arXiv:2208.02412\n\nPearl, J. (2000). Causality. Cambridge University Press, Cambridge.\n\nPlackett, R.L. (1953). The truncated Poisson distribution. Biometrics, 9, 485-488.\n\nRosenbaum, P. (2012). Design of Observational Studies. Springer, New York.\n\nRosenbaum, P. and Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.\n\nRoss, R. K., Su, I.-H., Webster-Clark, M., and Funk, M. J. (2022). Nondifferential treatment misclassification biases toward the null? Not a safe bet for active comparator studies. American Journal of Epidemiology, 191, 1917-1925.\n\nSaldana, D.F. and Feng, Y. (2018). SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models. Journal of Statistical Software, 83(2), 1–25.\n\nShortreed, S.M. and Ertefaie, A. (2017). Outcome-adaptive lasso: variable selection for\ncausal inference. Biometrics, 73(4), 1111-1122.\n\nSingh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., and Sellers, W.R. (2002). Gene expression correlates of clinical prostate cancer behavior Cancer Cell, 1(2), 203-209.\n\nShu, D. and Yi, G.Y. (2019a). Causal inference with measurement error in outcomes: Bias analysis and estimation methods. Statistical Methods in Medical Research, 28, 2049-2068.\n\nShu, D. and Yi, G.Y. (2019b). Inverse-probability-of-treatment weighted estimation of causal parameters in the presence of error-contaminated and time-dependent confounders. Biometrical Journal, 61, 1507-1525.\n\nTang, D., Kong, D., Pan, and W.,Wang, L. (2022). Ultra-high dimensional variable selection for doubly robust causal inference. Biometrics, 1-12. https://doi.org/10.1111/biom.13625\n\nVan Der Laan, M.J. and Robins, J.M. (2003). Unified Methods for Censored Longitudinal Data and Causality. Spring-Verlag, New York.\n\nVansteelandt, S., Bekaert, M., and Claeskens, G. (2010). On model selection and model misspecification in causal inference. Statistical Methods in Medical Research, 21, 7-30.\n\nYanagi, T. (2019). Inference on local average treatment effects for misclassified treatment. Econometric Reviews, 38, 938-960.\n\nYi, G.Y. (2017). Statistical Analysis with Measurement Error and Misclassication: Strategy, Method and Application. New York: Springer.\n\nYi, G.Y. and Chen, L.-P. (2023). Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders. Statistical Methods in Medical Research, 32, 691-711.\n\nZou, H., and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67, 301-320.; G0110354032; https://nccur.lib.nccu.edu.tw//handle/140.119/146313; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146313/1/403201.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/146313
https://nccur.lib.nccu.edu.tw/bitstream/140.119/146313/1/403201.pdf