A model averaging approach for estimating propensity scores by optimizing balance
暂无分享,去创建一个
Yuying Xie | Pan Wu | Yeying Zhu | Cecilia A Cotton | Yeying Zhu | C. Cotton | Yuying Xie | Pan Wu
[1] Jeffrey B. Birch,et al. Model robust regression: combining parametric, nonparametric, and semiparametric methods , 2001 .
[2] D. Ehrenthal,et al. Differences in the Protective Effect of Exclusive Breastfeeding on Child Overweight and Obesity by Mother’s Race , 2016, Maternal and Child Health Journal.
[3] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[4] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[5] J. N. K. Rao,et al. Bootstrap procedures for the pseudo empirical likelihood method in sample surveys , 2010 .
[6] Jens Hainmueller,et al. Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies , 2012, Political Analysis.
[7] T. Speed,et al. On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .
[8] L. Hansen. Large Sample Properties of Generalized Method of Moments Estimators , 1982 .
[9] J B Birch,et al. A semiparametric approach to analysing dose-response data. , 2000, Statistics in medicine.
[10] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[11] D. Rubin,et al. Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds , 2015 .
[12] Yeying Zhu,et al. Variable selection for propensity score estimation via balancing covariates. , 2015, Epidemiology.
[13] Mark J. van der Laan,et al. A semiparametric model selection criterion with applications to the marginal structural model , 2006, Comput. Stat. Data Anal..
[14] Elizabeth A Stuart,et al. Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. , 2010, Psychological methods.
[15] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .
[16] Ingram Olkin,et al. A Semiparametric Approach to Density Estimation , 1987 .
[17] J. Sekhon,et al. Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies , 2006, Review of Economics and Statistics.
[18] Brian K. Lee,et al. Weight Trimming and Propensity Score Weighting , 2011, PloS one.
[19] S. Carpino,et al. Group Prenatal Care: A Financial Perspective , 2015, Maternal and Child Health Journal.
[20] Thomas Lumley,et al. Analysis of Complex Survey Samples , 2004 .
[21] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[22] Elizabeth A Stuart,et al. Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. , 2013, Journal of clinical epidemiology.
[23] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[24] A. Eliakim,et al. Childhood obesity. , 2005, The Journal of clinical endocrinology and metabolism.
[25] P. GaileDaniel,et al. Estimating the arm-wise false discovery rate in array comparative genomic hybridization experiments. , 2007 .
[26] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[27] M. J. van der Laan,et al. Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .
[28] M. J. van der Laan,et al. Practice of Epidemiology Improving Propensity Score Estimators ’ Robustness to Model Misspecification Using Super Learner , 2015 .
[29] Changbao Wu,et al. Algorithms and R Codes for the Pseudo Empirical Likelihood Method in Survey Sampling , 2005 .
[30] D. Rubin. [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .
[31] Elizabeth A Stuart,et al. Improving propensity score weighting using machine learning , 2010, Statistics in medicine.
[32] D. Rubin. Causal Inference Using Potential Outcomes , 2005 .
[33] M. J. van der Laan. Targeted Estimation of Nuisance Parameters to Obtain Valid Statistical Inference , 2014, The international journal of biostatistics.
[34] D. McCaffrey,et al. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. , 2004, Psychological methods.
[35] J. Mark,et al. Targeted estimation of nuisance parameters to obtain valid statistical inference. , 2014 .
[36] Debashis Ghosh,et al. Estimating controlled direct effects of restrictive feeding practices in the ‘Early dieting in girls’ study , 2016, Journal of the Royal Statistical Society. Series C, Applied statistics.
[37] Jagbir Singh,et al. A Semiparametric Approach to Hazard Estimation with Randomly Censored Observations , 1997 .
[38] G. Imbens,et al. Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization , 2001, Health Services and Outcomes Research Methodology.
[39] L. Grummer-Strawn,et al. Does breastfeeding protect against pediatric overweight? Analysis of longitudinal data from the Centers for Disease Control and Prevention Pediatric Nutrition Surveillance System. , 2004, Pediatrics.
[40] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[41] Greg Ridgeway,et al. Toolkit for Weighting and Analysis of Nonequivalent Groups , 2014 .
[42] K. Imai,et al. Covariate balancing propensity score , 2014 .
[43] Henry Anhalt,et al. Consensus statement : Childhood obesity , 2005 .
[44] Ben Carterette,et al. Independent Relation of Maternal Prenatal Factors to Early Childhood Obesity in the Offspring , 2013, Obstetrics and gynecology.