BEST PRACTICES IN QUASI- EXPERIMENTAL DESIGNS Matching Methods for Causal Inference

[1]  E. Stuart,et al.  Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes. , 2008, Developmental psychology.

[2]  Gary King,et al.  Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.

[3]  Gary King,et al.  When Can History Be Our Guide? The Pitfalls of Counterfactual Inference , 2007 .

[4]  D. Rubin The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials , 2007, Statistics in medicine.

[5]  Michael E. Sobel,et al.  What Do Randomized Studies of Housing Mobility Demonstrate? , 2006 .

[6]  S. Raudenbush,et al.  Evaluating Kindergarten Retention Policy , 2006 .

[7]  S. Morgan,et al.  Matching Estimators of Causal Effects , 2006 .

[8]  Donald B. Rubin,et al.  Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions , 2006, math/0611263.

[9]  Peter C Austin,et al.  A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use , 2006, Statistics in medicine.

[10]  Jerome P. Reiter,et al.  A Comparison of Experimental and Observational Data Analyses , 2005 .

[11]  Rajeev Dehejia Practical propensity score matching: a reply to Smith and Todd , 2005 .

[12]  Donald B Rubin,et al.  On principles for modeling propensity scores in medical research , 2004, Pharmacoepidemiology and drug safety.

[13]  J. Lunceford,et al.  Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study , 2004, Statistics in medicine.

[14]  Kosuke Imai,et al.  Causal Inference With General Treatment Regimes , 2004 .

[15]  B. Hansen Full Matching in an Observational Study of Coaching for the SAT , 2004 .

[16]  G. Imbens,et al.  Implementing Matching Estimators for Average Treatment Effects in Stata , 2004 .

[17]  Jeffrey H Silber,et al.  Optimal multivariate matching before randomization. , 2004, Biostatistics.

[18]  M. Frölich Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators , 2004 .

[19]  Zhong Zhao,et al.  Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence , 2004, Review of Economics and Statistics.

[20]  Roberto Agodini,et al.  Are Experiments the Only Option? A Look at Dropout Prevention Programs , 2004, Review of Economics and Statistics.

[21]  Howard S. Bloom,et al.  Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs? , 2004, Review of Economics and Statistics.

[22]  Wei Lang,et al.  Examining the Impact of Missing Data on Propensity Score Estimation in Determining the Effectiveness of Self-Monitoring of Blood Glucose (SMBG) , 2001, Health Services and Outcomes Research Methodology.

[23]  Juwon Song,et al.  Handling Baseline Differences and Missing Items in a Longitudinal Study of HIV Risk Among Runaway Youths , 2001, Health Services and Outcomes Research Methodology.

[24]  D. Rubin Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation , 2001, Health Services and Outcomes Research Methodology.

[25]  Jennifer Hill,et al.  Reducing Bias in Treatment Effect Estimation in Observational Studies Suffering from Missing Data , 2004 .

[26]  D. McCaffrey,et al.  Propensity score estimation with boosted regression for evaluating causal effects in observational studies. , 2004, Psychological methods.

[27]  G. Imbens,et al.  Large Sample Properties of Matching Estimators for Average Treatment Effects , 2004 .

[28]  Steven Glazerman,et al.  Nonexperimental Versus Experimental Estimates of Earnings Impacts , 2003 .

[29]  N. Christakis,et al.  The health impact of health care on families: a matched cohort study of hospice use by decedents and mortality outcomes in surviving, widowed spouses. , 2003, Social science & medicine.

[30]  S. Greenland Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias , 2003, Epidemiology.

[31]  G. W. Imbens Sensitivity to Exogeneity Assumptions in Program Evaluation , 2003 .

[32]  Sascha O. Becker,et al.  Estimation of Average Treatment Effects Based on Propensity Scores , 2002 .

[33]  D. Rubin,et al.  Principal Stratification in Causal Inference , 2002, Biometrics.

[34]  Elaine L. Zanutto,et al.  Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse , 2001, Journal of the American Statistical Association.

[35]  C. Klaassen,et al.  Discussion to "Inference for semiparametric models: some questions and an answer" by Peter J. Bickel and Jaimyoung Kwon , 2001 .

[36]  Jeffrey A. Smith,et al.  Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators? , 2000 .

[37]  D. Rubin,et al.  Estimating and Using Propensity Scores with Partially Missing Data , 2000 .

[38]  D. Rubin,et al.  Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates , 2000 .

[39]  Xiao-Hua Zhou,et al.  The use of propensity scores in pharmacoepidemiologic research , 2000, Pharmacoepidemiology and drug safety.

[40]  Christopher Winship,et al.  THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA , 1999 .

[41]  G. Imbens The Role of the Propensity Score in Estimating Dose-Response Functions , 1999 .

[42]  R. D'Agostino Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[43]  James J. Heckman,et al.  Characterizing Selection Bias Using Experimental Data , 1998 .

[44]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator , 1998 .

[45]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[46]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme , 1997 .

[47]  Herbert L. Smith 6. Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies , 1997 .

[48]  D B Rubin,et al.  Matching using estimated propensity scores: relating theory to practice. , 1996, Biometrics.

[49]  D. Rubin,et al.  In utero exposure to phenobarbital and intelligence deficits in adult men. , 1995, JAMA.

[50]  J. Robins,et al.  Semiparametric Efficiency in Multivariate Regression Models with Missing Data , 1995 .

[51]  C. Drake Effects of misspecification of the propensity score on estimators of treatment effect , 1993 .

[52]  Paul R. Rosenbaum,et al.  Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms , 1993 .

[53]  Donald B. Rubin,et al.  Characterizing the effect of matching using linear propensity score methods with normal distributions , 1992 .

[54]  Donald B. Rubin,et al.  Affinely Invariant Matching Methods with Ellipsoidal Distributions , 1992 .

[55]  Roderick J. A. Little,et al.  Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics , 1992 .

[56]  P. Rosenbaum A Characterization of Optimal Designs for Observational Studies , 1991 .

[57]  P. Rosenbaum,et al.  Sensitivity analysis for matched case-control studies. , 1991, Biometrics.

[58]  Donald B. Rubin,et al.  Formal modes of statistical inference for causal effects , 1990 .

[59]  D. Rubin Multiple imputation for nonresponse in surveys , 1989 .

[60]  P. Rosenbaum Model-Based Direct Adjustment , 1987 .

[61]  P. Holland Statistics and Causal Inference , 1985 .

[62]  D. Rubin,et al.  The Bias Due to Incomplete Matching , 1985 .

[63]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .

[64]  R. Lalonde Evaluating the Econometric Evaluations of Training Programs with Experimental Data , 1984 .

[65]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[66]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[67]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[68]  D. Rubin,et al.  Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome , 1983 .

[69]  D. Basu,et al.  Randomization Analysis of Experimental Data: The Fisher Randomization Test Rejoinder , 1980 .

[70]  D. Basu Randomization Analysis of Experimental Data: The Fisher Randomization Test , 1980 .

[71]  D. Rubin,et al.  Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies , 1978 .

[72]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[73]  D. Rubin Assignment to Treatment Group on the Basis of a Covariate , 1976 .

[74]  Donald B. Rubin,et al.  Multivariate matching methods that are equal percent bias reducing , 1974 .

[75]  Donald B. Rubin,et al.  MULTIVARIATE MATCHING METHODS THAT ARE EQUAL PERCENT BIAS REDUCING, I: SOME EXAMPLES , 1974 .

[76]  D. Rubin INFERENCE AND MISSING DATA , 1975 .

[77]  D. Rubin Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[78]  W. G. Cochran,et al.  Controlling Bias in Observational Studies: A Review. , 1974 .

[79]  D. Rubin Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies , 1973 .

[80]  D. Rubin Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies , 1973 .

[81]  Donald B. Rubin,et al.  The Computerized Construction of a Matched Sample , 1970, American Journal of Sociology.

[82]  W. G. Cochran The effectiveness of adjustment by subclassification in removing bias in observational studies. , 1968, Biometrics.

[83]  E. C. Hammond,et al.  Smoking and lung cancer: recent evidence and a discussion of some questions. , 1959, Journal of the National Cancer Institute.

[84]  W. G. Cochran Analysis of covariance: Its nature and uses. , 1957 .

[85]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .

[86]  Oscar Kempthorne,et al.  Experimental Designs in Sociological Research. , 1949 .

[87]  Ernest Greenwood,et al.  Experimental sociology: A study in method , 1945 .