Unbiased Causal Inference From an Observational Study: Results of a Within-Study Comparison
暂无分享,去创建一个
Thomas D. Cook | Steffi Pohl | Peter M. Steiner | Renate Soellner | T. Cook | S. Pohl | R. Soellner | Jens Eisermann | Peter M Steiner | Jens Eisermann
[1] David E. Booth,et al. Analysis of Incomplete Multivariate Data , 2000, Technometrics.
[2] 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 .
[3] D. Watson,et al. Positive and negative affectivity and their relation to anxiety and depressive disorders. , 1988, Journal of abnormal psychology.
[4] D. Rubin. ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE , 1976 .
[5] Carl-Walter Kohlmann,et al. Untersuchungen mit einer deutschen Version der "Positive and Negative Affect Schedule" (PANAS). , 1996 .
[6] Donald B Rubin,et al. On principles for modeling propensity scores in medical research , 2004, Pharmacoepidemiology and drug safety.
[7] D. Rubin. Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation , 2001, Health Services and Outcomes Research Methodology.
[8] Peter M. Steiner,et al. The importance of covariate selection in controlling for selection bias in observational studies. , 2010, Psychological methods.
[9] J. Schafer,et al. Average causal effects from nonrandomized studies: a practical guide and simulated example. , 2008, Psychological methods.
[10] J. Lunceford,et al. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study , 2004, Statistics in medicine.
[11] W. G. Cochran,et al. Controlling Bias in Observational Studies: A Review. , 1974 .
[12] Donald B. Rubin,et al. Comment: The Design and Analysis of Gold Standard Randomized Experiments , 2008 .
[13] T. Speed,et al. On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .
[14] D. Rubin. Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies , 1973 .
[15] Walter Zucchini,et al. Model Selection , 2011, International Encyclopedia of Statistical Science.
[16] Joshua D. Angrist,et al. Identification of Causal Effects Using Instrumental Variables , 1993 .
[17] P. Holland. Statistics and Causal Inference , 1985 .
[18] 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.
[19] Juan-José Díaz,et al. An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator , 2005, The Journal of Human Resources.
[20] D. Watson,et al. Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.
[21] Donald B. Rubin,et al. Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .
[22] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .
[23] Gary King,et al. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.
[24] D. Rubin. Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies , 1973 .
[25] R. Lalonde. Evaluating the Econometric Evaluations of Training Programs with Experimental Data , 1984 .
[26] Arthur S. Goldberger,et al. Selection bias in evaluating treatment effects: Some formal illustrations , 2008 .
[27] G. Imbens,et al. On the Failure of the Bootstrap for Matching Estimators , 2006 .
[28] Vivian C. Wong,et al. Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within‐study comparisons , 2008 .
[29] Peter M. Steiner,et al. Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments , 2008 .
[30] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .
[31] D B Rubin,et al. Matching using estimated propensity scores: relating theory to practice. , 1996, Biometrics.
[32] H. Harman,et al. Kit of factor-referenced cognitive tests , 1976 .
[33] T. Shakespeare,et al. Observational Studies , 2003 .
[34] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[35] J. Robins,et al. Semiparametric Efficiency in Multivariate Regression Models with Missing Data , 1995 .
[36] Joseph Kang,et al. Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data , 2007, 0804.2958.
[37] William M. K. Trochim,et al. Research Design for Program Evaluation: The Regression-Discontinuity Approach , 1984 .
[38] Gary King,et al. Misunderstandings between experimentalists and observationalists about causal inference , 2008 .
[39] W. G. Cochran. The effectiveness of adjustment by subclassification in removing bias in observational studies. , 1968, Biometrics.
[40] D. Rubin. For objective causal inference, design trumps analysis , 2008, 0811.1640.
[41] Steven Glazerman,et al. Nonexperimental Versus Experimental Estimates of Earnings Impacts , 2003 .
[42] D B Rubin,et al. Estimating the causal effects of smoking , 2001, Statistics in medicine.