Abadie, A. (2002), “Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models,” Journal of the American Statistical Association, Vol. 97(457): 284–292.Google Scholar

Abadie, A. (2003), “Semiparametric Instrumental Variable Estimation of Treatment Response Models,” Journal of Econometrics, Vol. 113(2): 231–263.Google Scholar

Abadie, A. (2005): “Semiparametric Difference-in-Differences Estimators,” Review of Economic Studies, Vol. 72(1): 1–19.Google Scholar

Abadie, A., J., Angrist, and G., Imbens, (2002), “Instrumental Variables Estimation of Quantile Treatment Effects,” Econometrica, Vol. 70(1): 91–117.Google Scholar

Abadie, A., A., Diamond, and J., Hainmueller, (2010), “Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program,” Journal of the American Statistical Association, Vol. 105(490): 493–505.Google Scholar

Abadie, A., D., Drukker, H., Herr, and G., Imbens, (2003), “Implementing Matching Estimators for Average Treatment Effects in STATA,” The STATA Journal, Vol. 4(3): 290–311.Google Scholar

Abadie, A., and G., Imbens, (2006), “Large Sample Properties of Matching Estimators for Average Treatment Effects,” Econometrica, Vol. 74(1): 235–267.Google Scholar

Abadie, A., and G., Imbens, (2008), “On the Failure of the Bootstrap for Matching Estimators,” Econometrica, Vol. 76(6): 1537–1557.Google Scholar

Abadie, A., and G., Imbens, (2009), “Bias-Corrected Matching Estimators for Average Treatment Effects,” Journal of Business and Economic Statistics, Vol. 29(1): 1–11.Google Scholar

Abadie, A., and G., Imbens, (2010), “Estimation of the Conditional Variance in Paired Experiments,” Annales d'Economie et de Statistique, Vol. 91: 175–187.Google Scholar

Abadie, A., and G., Imbens, (2011), “Bias-Corrected Matching Estimators for Average Treatment Effects,” Journal of Business and Economic Statistics, Vol. 29(1): 1–11.Google Scholar

Abadie, A., and G., Imbens, (2012), “Matching on the Estimated Propensity Score,” National Bureau of Economic Research Working paper 15301.

Abbring, J., and G., van den Berg, (2003), “The Nonparametric Identification of Treatment Effects in Duration Models,” Econometrica, Vol. 71(5): 1491–1517.Google Scholar

Althauser, R., and D., Rubin, (1970), “The Computerized Construction of a Matched Sample,” The American Journal of Sociology, Vol. 76(2): 325–346.Google Scholar

Altman, D., (1991), Practical Statistics for Medical Research, Chapman and Hall/CRC.

Angrist, J. (1990), “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records,” American Economic Review, Vol. 80: 313–335.Google Scholar

Angrist, J. (1998), “Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants,” Econometrica, Vol. 66(2): 249–288.Google Scholar

Angrist, J. D., and J., Hahn, (2004) “When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects,” Review of Economics and Statistics, Vol. 86(1): 58–72.Google Scholar

Angrist, J., G., Imbens, and D., Rubin, (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, Vol. 91: 444–472.Google Scholar

Angrist, J., and A., Krueger, (1999), “Does Compulsory Schooling Affect Schooling and Earnings,” Quarterly Journal of Economics, Vol. CVI(4): 979–1014.Google Scholar

Angrist, J. D., and A. B., Krueger, (2000), “Empirical Strategies in Labor Economics,” in A., Ashenfelter and D., Card, eds. Handbook of Labor Economics, vol. 3. Elsevier Science.

Angrist, J. D., and G. M., Kuersteiner, (2011), “Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score,” Review of Economics and Statistics, Vol. 93(3): 725–747.Google Scholar

Angrist, J., and V., Lavy, (1999), “Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement,” Quarterly Journal of Economics, Vol. CXIV: 1243.Google Scholar

Angrist, J., and S., Pischke, (2008), Mostly Harmless Econometrics: An Empiricists' Companion, Princeton University Press.

Anscombe, F. J. (1948), “The Validity of Comparative Experiments,” Journal of the Royal Statistical Society, Series A, Vol. 61: 181–211.Google Scholar

Ashenfelter, O. (1978), “Estimating the Effect of Training Programs on Earnings,” Review of Economics and Statistics, Vol. 60: 47–57.Google Scholar

Ashenfelter, O., and D., Card, (1985), “Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs,” Review ofEconomics and Statistics, Vol. 67: 648–660.Google Scholar

Athey, S., and G., Imbens, (2006), “Identification and Inference in Nonlinear Difference-In Differences Models,” Econometrica, Vol. 74(2): 431–497.Google Scholar

Athey, S., and G., Imbens, (2014), “Supervised Learning Methods for Causal Effects” Unpublished Manuscript.

Athey, S., and S., Stern, (1998), “An Empirical Framework for Testing Theories About Complementarity in Organizational Design,” NBER working paper 6600.

Austin, P. (2008), “A Critical Appraisal of Propensity-Score Matching in the Medical Literature Between 1996 and 2003,” Statistics in Medicine, Vol. 27: 2037–2049.Google Scholar

Baiocchi, M., D., Small, S., Lorch, and P., Rosenbaum, (2010), “Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants,” The Journal of the American Statistical Association, Vol. 105(492): 1285–1296.Google Scholar

Baker, S. (2000), “Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None Compliance,” The Journal of the American Statistical Association, Vol. 95(449): 43–50.Google Scholar

Ball, S., G., Bogatz D., Rubin, and A., Beaton, (1973), “Reading with Television: An Evaluation of The Electric Company. A Report to the Children's Television Workshop,” Vol 1 and 2, Educational Testing Service, Princeton NJ.

Barnard, J., J., Du, J., Hill, and D., Rubin, (1998), “A Broader Template for Analyzing Broken Randomized Experiments,” Sociological Methods & Research, Vol. 27: 285–317.Google Scholar

Barnow, B. S., G. G., Cain, and A. S., Goldberger, (1980), “Issues in the Analysis of Selectivity Bias,” in E., Stromsdofer and G., Farkas, eds. Evaluation Studies, vol.5, Sage.

Becker, S., and A., Ichino, (2002), “Estimation of Average Treatment Effects Based on Propensity Scores,” The Stata Journal, Vol. 2(4): 358–377.Google Scholar

Beebee, H., C., Hitchcock, and P., Menzies, (2009), The Oxford Handbook of Causation, Oxford University Press.

Belloni, A., V., Chernozhukov, and C., Hansen, (2014), “Inference on Treatment Effects After Selection Amongst High-Dimensional Controls,” The Review of Economic Studies, Vol. 81(2): 608–650.Google Scholar

Bertrand, M., and S., Mullainathan, (2004), “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination,” American Economic Review, Vol. 94(4): 991–1013.Google Scholar

Bitler, M., J., Gelbach, and H., Hoynes, (2002), “What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments,” American Economic Review, Vol. 96(4): 988–1012.Google Scholar

Björklund, A., and R., Moffitt, (1987), “The Estimation of Wage Gains and Welfare Gains in Self–Selection Models,” Review ofEconomics and Statistics, Vol. LXIX: 42–49.Google Scholar

Black, S. (1999), “Do Better Schools Matter? Parental Valuation of Elementary Education,” Quarterly Journal ofEconomics, Vol. CXIV: 577.Google Scholar

Bloom, H. (1984), “Accounting for No-Shows in Experimental Evaluation Designs,” Evaluation Review, Vol. 8: 225–246.Google Scholar

Blundell, R., and M., Costa-Dias, (2000), “Evaluation Methods for Non-Experimental Data,” Fiscal Studies, Vol. 21(4): 427–468.Google Scholar

Blundell, R., and M., Costa-Dias, (2002), “Alternative Approaches to Evaluation in Empirical Microeconomics,” Portuguese Economic Journal, Vol. 1(1): 91–115.Google Scholar

Blundell, R., A., Gosling, H., Ichimura, and C., Meghir, (2007), “Changes in the Distribution of Male and Female Wages Accounting for the Employment Composition,” Econometrica, Vol. 75(2): 323–363.Google Scholar

Box, G., S., Hunter, and W., Hunter, (2005), Statistics for Experimenters: Design, Innovation and Discovery, Wiley.

Box, G., and G., Tiao, (1973), Bayesian Inference in Statistical Analysis, Addison Wesley.

Breiman, L., and P., Spector, (1992), “Submodel Selection and Evaluation in Regression: The x-Random Case,” International Statistical Review, Vol. 60: 291–319.Google Scholar

Brillinger, D. R., Jones, L. V., and Tukey, J. W. (1978), “Report of the statistical task force for the weather modification advisory board.” The Management of Western Resources, Vol. II: The Role of Statistics on Weather Resources Management. Stock No. 003-018-00091-1, Government Printing Office, Washington, DC.

Brooks, S., A., Gelman, G., Jones, and X.-Li., Meng, (2011), Handbook of Markov Chain Monte Carlo, Chapman and Hall.

Bühlman, P., and S., van der Geer, (2011), Statistics for High-Dimensional Data: Methods, Theory and Applications, Springer Verlag.

Busso, M., J., DiNardo, and J., McCrary, (2009), “New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators,” Unpublished Working Paper.

Caliendo, M. (2006), Microeconometric Evaluation of Labour Market Policies, Springer Verlag.

Card, D. (1995), “Using Geographic Variation in College Proximity to Estimate the Return to Schooling,” in Christofides, E. K., Grant, and R., Swidinsky, ed. Aspects of Labor Market Behaviour: Essays in Honour of John Vanderkamp, University of Toronto Press.

Card, D., and A., Krueger, (1994), “Minimum Wages and Employment: A Case Study of the Fast-food Industry in New Jersey and Pennsylvania,” American Economic Review, Vol. 84(4): 772–784.Google Scholar

Card, D., and D., Sullivan, (1988), “Measuring the Effect of Subsidized Training Programs on Movements In and Out of Employment,” Econometrica, Vol. 56(3), 497–530.Google Scholar

Chernozhukov, V., and C., Hansen, (2005), “An IV Model of Quantile Treatment Effects,” Econometrica, Vol. 73(1): 245–261.Google Scholar

Chetty, R., J., Friedman N., Hilger E., Saez D., Schanzenbach, and D., Yagan, (2011), “How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project STAR,” Quarterly Journal of Economics, Vol. 126(4): 1593–1660.Google Scholar

Clogg, C., D., Rubin, N., Schenker, B., Schultz, and L., Weidman, (1991), “Multiple Imputation of Industry and Occupation Codes in Census Public-Use Samples Using Bayesian Logistic Regression,”, Journal of the American Statistical Association, Vol. 86(413): 68–78.Google Scholar

Cochran, W. G. (1965), “The Planning of Observational Studies of Human Populations,” Journal of the Royal Statistical Society, Series A (General), Vol. 128(2): 234–266.Google Scholar

Cochran, W. G. (1968) “The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies,” Biometrics, Vol. 24: 295–314.Google Scholar

Cochran, W. G. (1977), Sampling Techniques, Wiley.

Cochran, W. G., and G., Cox, (1957), Experimental Design, Wiley Classics Library.

Cochran, W. G., and D., Rubin, (1973), “Controlling Bias in Observational Studies: A Review,” Sankhya, Vol. 35: 417–46.Google Scholar

Cook, T. (2008), “‘Waiting for Life to Arrive’: A History of the Regression-Discontinuity Design in Psychology, Statistics, and Economics,” Journal of Econometrics, Vol. 142(2): 636–654.Google Scholar

Cook, T., and D., DeMets, (2008), Introduction to Statistical Methods for Clinical Trials, Chapman and Hall/CRC.

Cornfield et al. (1959), “Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions,” Journal of the National Cancer Institute, Vol. 22: 173–203.

Cox, D. (1956), “A Note on Weighted Randomization,” The Annals of Mathematical Statistics, Vol. 27(4): 1144–1151.Google Scholar

Cox, D. (1958), Planning of Experiments, Wiley Classics Library.

Cox, D. (1992), “Causality: Some Statistical Aspects,” Journal of the Royal Statistical Society, Series A, Vol. 155: 291–301.Google Scholar

Cox, D., and P., McCullagh, (1982), “Some Aspects of Covariance,” (with discussion). Biometrics, Vol. 38: 541–561.Google Scholar

Cox, D., and N., Reid, (2000), The Theory of the Design of Experiments, Chapman and Hall/CRC.

Crump, R., V. J., Hotz, G., Imbens, and O., Mitnik, (2008), “Nonparametric Tests for Treatment Effect Heterogeneity,” Review of Economics and Statistics, Vol. 90(3): 389–405.Google Scholar

Crump, R., V. J., Hotz, G., Imbens, and O., Mitnik, (2009), “Dealing with Limited Overlap in Estimation of Average Treatment Effects,” Biometrika, Vol. 96: 187–99.Google Scholar

Cuzick, J., R., Edwards, and N., Segnan, (1997), “Adjusting for Non-Compliance and Contamination in Randomized Clinical Trials,” Statistics in Medicine, Vol. 16: 1017–1039.Google Scholar

Darwin, C., (1876), The Effects of Cross- and Self-Fertiilisation in the Vegetable Kingdom, John Murry.

Davies, O. (1954), The Design and Analysis of Industrial Experiments, Oliver and Boyd.

Dawid, P. (1979), “Conditional Independence in Statistical Theory,” Journal of the Royal Statistical Society, Series B, Vol. 41(1): 1–31.Google Scholar

Dawid, P. (2000), “Causal Inference Without Counterfactuals,” Journal of the American Statistical Association, Vol. 95(450): 407–424.Google Scholar

Deaton, A. (2010), “Instruments, Randomization, and Learning about Development,” Journal of Economic Literature, Vol. 48(2): 424–455.Google Scholar

Dehejia, R. (2002), “Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs with Grouped Data,” Journal of Business and Economic Statistics, Vol. 21(1): 1–11.Google Scholar

Dehejia, R. (2005a), “Practical Propensity Score Matching: A Reply to Smith and Todd,” Journal of Econometrics, Vol. 125: 355–364.Google Scholar

Dehejia, R. (2005b) “Program Evaluation as a Decision Problem,” Journal of Econometrics, Vol. 125: 141–173.Google Scholar

Dehejia, R., and S., Wahba, (1999), “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs,” Journal of the American Statistical Association, Vol. 94: 1053–1062.Google Scholar

Dehejia, R., and S., Wahba, (2002), “Propensity Score-Matching Methods for Nonexperimental Causal Studies,” Review of Economics and Statistics, Vol. 84(1): 151–161.Google Scholar

Diaconis, P. (1976), “Finite Forms of de Finetti's Theorem on Exchangeability,” Technical Report 84, Department of Statistics, Stanford University.

Diamond, A., and J., Sekhon, (2013), “Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies,” Review of Economics and Statistics, Vol. 95(3): 932–945.Google Scholar

Diehr, P., D., Martin, T., Koepsell, and A., Cheadle, (1995), “Breaking the Matches in a Paired t-Test for Community Interventions When the Number of Pairs is Small,” Statistics in Medicine, Vol. 14: 1491–1504.Google Scholar

Donner, A. (1987), “Statistical Methodology for Paired Cluster Designs,” American Journal of Epidemiology, Vol. 126(5), 972–979.Google Scholar

Du, J. (1998) “Valid Inferences After Propensity Score Subclassification Using Maximum Number of Subclasses as Building Blocks,” PhD Thesis, Department of Statistics, Harvard University.

Duflo, E., R., Hanna, and S., Ryan, (2012), “Incentives Work: Getting Teachers to Come to School,” American Economic Review, Vol. 102(4): 1241–1278.Google Scholar

Efron, B., and D., Feldman, (1992), “Compliance as an Explanatory Variable in Clinical Trials,” Journal of the American Statistical Association, Vol. 86(413): 9–17.Google Scholar

Efron, B., and R., Tibshirani, (1993), An Introduction to the Bootstrap, Chapman and Hall.

Engle, R., D., Hendry, and J.-F., Richard, (1974) “Exogeneity,” Econometrica, Vol. 51(2): 277–304.Google Scholar

Espindle, L. (2004), “Improving Confidence Coverage for the Estimate of the Treatment Effect in a Subclassification Setting,” Undergraduate Thesis, Department of Statistics, Harvard University.

de Finetti, B. (1964), “Foresignt: Its Logical Laws, Its Subjective Sources,” in Kyburg and Smokler, eds. Studies in Subjective Probability, Wiley.

de Finetti, B. (1992), Theory of Probability: A Critical Introductory Treatment, Vol. 1 & 2, Wiley Series in Probability & Mathematical Statistics.

Feller, W. (1965), An Introduction to Probability and its Applications, Vol. 1, John Wiley and Sons, New York City.

Firpo, S. (2003), “Efficient Semiparametric Estimation of Quantile Treatment Effects”, PhD Thesis, Chapter 2, Department of Economics, University of California, Berkeley.

Firpo, S. (2007), “Efficient Semiparametric Estimation of Quantile Treatment Effects,” Econometrica, Vol. 75(1): 259–276.Google Scholar

Fisher, L., D., Dixon, J., Herson, R., Frankowski, M., Hearron, and K., Peace, (1990), “Intention to Treat in Clinical Trials”, in Peace, ed. Statistical Issues in Drug Research and Development, Marcel Dekker, Inc.

Fisher, R. A. (1918), “The Causes of Human Variability,” Eugenics Review, Vol. 10: 213–220.Google Scholar

Fisher, R. A. (1925), Statistical Methods for Research Workers, 1st ed, Oliver and Boyd.

Fisher, R. A. (1935), Design of Experiments, Oliver and Boyd.

Fisher, R., and W., MacKenzie, (1923), “Studies in Crop Vacation. II. The Manurial Response of Different Potato Varieties,” Journal of Agricultural Science, Vol. 131: 311–320.Google Scholar

Fisher, L. et al. (1990), “Intention-to-Treat in Clinical Trials,” in K.E., Peace ed., Statistical Issues in Drug Research and Development, Marcel Dekker.

Fraker, T., and R., Maynard, (1987), “The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs,” Journal of Human Resources, Vol. 22(2): 194–227.Google Scholar

Frangakis, C., and D., Rubin, (2002), “Principal Stratification,” Biometrics, Vol. 58(1): 21–29.Google Scholar

Freedman, D. A. (2006), “Statistical Models for Causation: What Inferential Leverage Do They Provide”, Evaluation Review, Vol. 30(6): 691–713.Google Scholar

Freedman, D. A. (2008a), “On Regression Adjustmens to Experimental Data”, Advances in Applied Mathematics, Vol. 30(6): 180–193.Google Scholar

Freedman, D. A. (2008b), “On Regression Adjustmens in Experiments with Several Treatments,” Annals of Applied Statistics, Vol. 2: 176–196.Google Scholar

Freedman, D. A. (2009), in D., Collier, J. S., Sekhon, and P. B., Stark, eds. Statistical Models and Causal Inference: A Diagogue with the Social Sciences, Cambridge University Press.

Freedman, D. A., Pisani, R. and Purves, R. (1978). Statistics, Norton.

Friedlander, D., and J., Gueron, (1995), “Are High-Cost Services More Effective Than Low-Cost Services,” in C., Manski and I., Garfinkel, eds. Evaluating Welfare and Training Programs, Harvard University Press, pp. 143–198.

Friedlander, D., and P., Robins, (1995), “Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods,” American Economic Review, Vol. 85:923–937.Google Scholar

Frölich, M. (2000), “Treatment Evaluation: Matching versus Local Polynomial Regression,” Discussion paper 2000-17, Department of Economics, University of St. Gallen.

Frölich, M. (2004a), “Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators,” The Review of Economics and Statistics, Vol. 86(1): 77–90.Google Scholar

Frölich, M. (2004b), “A Note on the Role of the Propensity Score for Estimating Average Treatment Effects,” Econometric Reviews, Vol. 23(2): 167–174.Google Scholar

Frumento, P., F., Mealli, B., Pacini, and D., Rubin, (2012), “Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data,” Journal of the American Statistical Association, No. 498: 450–466.Google Scholar

Gail, M. H., S., Mark, R., Carroll, S., Green, and D., Pee, (1996), “On Design Considerations and Randomization-based Inference for Coomunity Intervention Trials,” Statistics in Medicine, Vol. 15: 1069–1092.Google Scholar

Gail, M. H., W., Tian, and S., Piantadosi, (1988), “Tests for No Treatment Effect in Randomized Clinical Trials,” Biometrika, Vol. 75(3): 57–64.Google Scholar

Gail, M. H., S., Wieand, and S., Piantadosi, (1984), “Biased Estimates of Treatment Effect in Randomized Experiments with Nonlinear Regressions and Omitted Covariates,” Biometrika, Vol. 71(3): 431–444.Google Scholar

Gelman, A., J., Carlin, H., Stern, and D., Rubin, (1995), Baeysian Data Analysis, Chapman and Hall.

Gelman, A., and J., Hill, (2006), Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press.

Gill, R., and J., Robins. (2001), “Causal Inference for Complex Longitudinal Data: The Continuous Case,” Annals of Statistics, Vol. 29(6): 1785–1811.Google Scholar

Goldberger, A. (1991), A Course in Econometrics, Harvard University Press.

Graham, B., (2008), “Identifying Social Interactions through Conditional Variance Restrictions,” Econometrica, Vol. 76(3): 643–660.Google Scholar

Granger, C. (1969), “Investigating Causal Relations by Econometric Models and Cross-spectral Methods,” Econometrica, Vol. 37(3): 424–438.Google Scholar

Greene, W. (2011), Econometric Analysis, 7th Edition, Prentice Hall.

Gu, X., and P., Rosenbaum, (1993), “Comparison of Multivariate Matching Methods: Structures, Distances and Algorithms,” Journal of Computational and Graphical Statistics, Vol. 2: 405–420.Google Scholar

Guo, S., and M., Fraser, (2010), Propensity Score Analysis, Sage Publications.

Gutman, R., and D., Rubin, (2014), “Robust Estimation of Causal Effects of Binary Treatments in Unconfounded Studies with Dichotomous Outcomes”, Statistics in Medicine, forthcoming.

Haavelmo, T. (1943), “The Statistical Implications of a System of Simultaneous Equations,” Econometrica, Vol. 11(1):1–12.Google Scholar

Haavelmo, T. (1944), “The Probability Approach in Econometrics,” Econometrica, Vol. 11.Google Scholar

Hahn, J. (1998), “On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects,” Econometrica, Vol. 66(2): 315–331.Google Scholar

Hahn, J., P., Todd, and W., VanderKlaauw, (2000), “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,” Econometrica, Vol. 69(1): 201–209.Google Scholar

Hainmueller, J. (2012), “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies,” Political Analysis, Vol. 20: 25–46.Google Scholar

Ham, J., and R., Lalonde, (1996), “The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training,” Econometrica, Vol. 64: 1.Google Scholar

Hansen, B. (2007), “Optmatch: Flexible, Optimal Matching for Observational Studies,” R News, Vol. 7(2): 18–24.Google Scholar

Hansen, B. (2008), “The Prognostic Analogue of the Propensity Score,” Biometrika, Vol. 95(2): 481–488.Google Scholar

Hansen, B., and S., Klopfer, (2006), “Optimal Full Matching and Related Designs via Network Flows,” Journal of Computational and Graphical Statistics, Vol. 15(3): 609–627.Google Scholar

Hartigan, J. (1983), Bayes Theory, Springer Verlag.

Hartshorne, C., and P., Weiss, (Eds.). (1931). Collected Papers of Charles Sanders Peirce (Vol. 1), Harvard University Press.

Hearst, N., Newman, T., and S., Hulley, (1986), “Delayed Effects of the Military Draft on Mortality: A Randomized Natural Experiment,” New England Journal of Medicine, Vol. 314 (March 6): 620–624.Google Scholar

Heckman, J., and J., Hotz, (1989), “Alternative Methods for Evaluating the Impact of Training Programs,” (with discussion), Journal of the American Statistical Association, Vol. 84(804): 862–874.Google Scholar

Heckman, J., H., Ichimura, and P., Todd, (1997), “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program,” Review of Economic Studies, Vol. 64: 605–654.Google Scholar

Heckman, J., H., Ichimura, and P., Todd, (1998), “Matching as an Econometric Evaluation Estimator,” Review of Economic Studies, Vol. 65: 261–294.Google Scholar

Heckman, J., H., Ichimura, J., Smith, and P., Todd, (1998), “Characterizing Selection Bias Using Experimental Data,” Econometrica, Vol. 66: 1017–1098.Google Scholar

Heckman, J., R., Lalonde, and J., Smith, (2000), “The Economics and Econometrics of Active Labor Markets Programs,” in A., Ashenfelter and D., Card, eds. Handbook of Labor Economics, vol. 3, Elsevier Science.

Heckman, J., and R., Robb, (1984), “Alternative Methods for Evaluating the Impact of Interventions,” in Heckman and Singer eds., Longitudinal Analysis of Labor Market Data, Cambridge University Press.

Heckman, J., and E., Vytlacil, (2007a), “Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation,” in J., Heckman and E., Leamer, eds. Handbook of Econometrics, vol. 6B, Chapter 70, 4779–4874, Elsevier Science.

Heckman, J., and E., Vytlacil, (2007b), “Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments,” in J., Heckman and E., Leamer, eds. Handbook of Econometrics, vol. 6B, Chapter 71, 4875–5143, Elsevier Science.

Heitjan, D., and R., Little, (1991), “Multiple Imputation for the Fatal Accident Reporting System,” Applied Statistics, Vol. 40: 13–29.Google Scholar

Hendry, D., and Morgan, M. (1995). The Foundations of Econometric Analysis, Cambridge University Press.

Hewitt, E., and L., Savage, (1955), “Symmetric Measures on Cartesian Products,” Transactions of the American Mathematical Society, Vol. 80: 470–501.Google Scholar

Hinkelmann, K., and O., Kempthorne, (2005), Design and Analysis of Experiments, Vol.2, Advance Experimental Design, Wiley.

Hinkelmann, K., and O., Kempthorne, (2008), Design and Analysis of Experiments, Vol.1, Introduction to Experimental Design, Wiley.

Hirano, K., and G., Imbens, (2001), “Estimation of Causal Effects Using Propensity Score Weighting: An Application of Data on Right Heart Catherization,” Health Services anfOutcomes Research Methodology, Vol. 2: 259–278.Google Scholar

Hirano, K., and G., Imbens, (2004), “The Propensity Score with Continuous Treatments,” in Gelman and Meng, eds. Applied Bayesian Modelling and Causal Inference from Missing Data Perspectives, Wiley.

Hirano, K., G., Imbens, and G., Ridder, (2003), “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” Econometrica, Vol. 71(4): 1161–1189.Google Scholar

Hirano, K., G., Imbens, D., Rubin, and A., Zhou, (2000), “Estimating the Effect of Flu Shots in a Randomized Encouragement Design,” Biostatistics, Vol. 1(1): 69–88.Google Scholar

Ho, D., and K., Imai, (2006), “Randomization Inference with Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall Election,” Journal of the American Statistical Association, Vol. 101(476): 888–900.Google Scholar

Ho, D., K., Imai, G., King, and E., Stuart, (2007), “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference,” Political Analysis, Vol. 81: 945–970.Google Scholar

Hodges, J. L., and Lehmann, E., (1970), Basic Concepts of Probability and Statistics, 2nd ed., Holden-Day.

Holland, P. (1986), “Statistics and Causal Inference” (with discussion), Journal of the American Statistical Association, Vol. 81: 945–970.Google Scholar

Holland, P. (1988), “Causal Inference, Path Analysis, and Recursive Structural Equations Models”, (with discussion), Sociological Methodology, Vol. 18: 449–484.Google Scholar

Holland, P., and D., Rubin, (1982), “Introduction: Research on Test Equating Sponsored by Educational Testing Service, 1978–1980,” in Test Equating, Academic Press Inc. pp. 1–6.

Holland, P., and D., Rubin, (1983), “On Lord's Paradox,” in Wainer and Messick, eds. Principles of Modern Psychological Measurement: A Festschrift for Frederick Lord, Erlbaum, pp. 3–25.

Hood, and T., Koopmans, (1953), Studies in Econometric Method, Wiley, New York.

Horowitz, J. (2002), “The Bootstrap,” in Heckman and Leamer, eds. Handbook of Econometrics, Vol. 5, Elsevier.

Horvitz, D., and D., Thompson, (1952), “A Generalization of Sampling Without Replacement from a Finite Universe,” Journal of the American Statistical Association, Vol. 47: 663–685.Google Scholar

Hotz, V. J., G., Imbens, and J., Klerman, (2001), “The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program,” Journal of Labor Economics, Vol. 24(3): 521–566.Google Scholar

Hotz, J., G., Imbens, and J., Mortimer, (2005), “Predicting the Efficacy of Future Training Programs Using Past Experiences,” Journal of Econometrics, Vol. 125: 241–270.Google Scholar

Huber, M., M., Lechner, and C., Wunsch, (2012), “The Performance of Estimators Based on the Propensity Score,” Journal of Econometrics, Vol. 175(1): 1–21.Google Scholar

Imai, K. (2008). Variance Identification and Efficiency Analysis in Randomized Experiments under the Matched-Pair Design.” Statistics in Medicine, Vol. 27(24) (October): 4857–4873.Google Scholar

Imai, K., and D., van Dyk, (2004), “Causal Inference with General Treatment Regimes: Generalizing the Propensity Score,” Journal of the American Statistical Assocation, Vol. 99: 854–866.Google Scholar

Imai, K., G., King, and E. A., Stuart, (2008), “Misunderstandings among Experimentalists and Observationalists about Causal Inference,” Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171(2): 481–502.Google Scholar

Imbens, G. (2000), “The Role of the Propensity Score in Estimating Dose-Response Functions,” Biometrika, Vol. 87(3): 706–710.Google Scholar

Imbens, G. (2003), “Sensivity to Exogeneity Assumptions in Program Evaluation,” American Economic Review, Papers and Proceedings.Google Scholar

Imbens, G. (2004), “Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review,” Review of Economics and Statistics, Vol. 86(1): 1–29.Google Scholar

Imbens, G. (2010), “Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009),” Journal of Economic Literature, Vol. 48(2): 399–423.Google Scholar

Imbens, G. (2011), “On the Finite Sample Benefits of Stratification, Blocking and Pairing in Randomized Experiments,” Unpublished Manuscript.

Imbens, G. (2014), “Instrumental Variables: An Econometrician's Perspective,” Statistical Science, Vol. 29(3): 375–379.Google Scholar

Imbens, G., (2015), “Matching Methods in Practice: Three Examples,” forthcoming, Journal of Human Resources.Google Scholar

Imbens, G., and J., Angrist, (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, Vol. 61(2): 467–476.Google Scholar

Imbens, G., and K., Kalyanaraman, (2012), “Optimal Bandwidth Choice for the Regression Discontinuity Estimator Review of Economic Studies,” Review of Economic Studies, Vol. 79(3): 933–959.Google Scholar

Imbens, G., and T., Lemieux, (2008), “Regression Discontinuity Designs: A Guide to Practice,” Journal of Econometrics, Vol. 142(2): 615–635.Google Scholar

Imbens, G., and P., Rosenbaum, (2005), “Randomization Inference with an Instrumental Variable,” Journal of the Royal Statistical Society, Series A, Vol. 168(1): 109–126.Google Scholar

Imbens, G., and D., Rubin, (1997a), “Estimating Outcome Distributions for Compliers in Instrumental Variable Models,” Review of Economic Studies, Vol. 64(3): 555–574.Google Scholar

Imbens, G., and D., Rubin, (1997b), “Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance,” Annals of Statistics, Vol. 25(1): 305–327.Google Scholar

Imbens, G., and J., Wooldridge, (2009), “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, Vol. 47(1): 1–81.Google Scholar

Jin, H., and D. B., Rubin, (2008), “Principal Stratification for Causal Inference with Extended Partial Compliance: Application to Efron-Feldman Data,” Journal ofthe American Statistical Association, Vol. 103: 101–111.Google Scholar

Kane, T., and C., Rouse, (1995), “Labor-Market Returns to Two- and Four- Year College,” American Economic Review, Vol. 85(3): 600–614.Google Scholar

Kang, J., and J., Schafer, (2007), “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data,” Statistical Science, Vol. 22(4): 523–539.Google Scholar

Kempthorne, O. (1952), The Design and Analysis of Experiments, Robert Krieger Publishing Company.

Kempthorne, O. (1955), “The Randomization Theory of Experimental Evidence,” Journal of the American Statistical Association, Vol. 50927(1): 946–967.Google Scholar

Ketel, N., E., Leuven, H., Oosterbeek, and B., VanderKlaauw, (2013), “The Returns to Medical School in a Regulated Labor Market: Evidence from Admission Lotteries,” Unpublished Manuscript.

Koch, G., C., Tangen, J. W, Jung, and I., Amara, (1998), “Issues for Covariance Analysis of Dichotomous and Orderd Categorical Data from Randomized Clinical Trials and Non-Parametric Strategies for Addressing Them,” Statistics in Medicine, Vol. 17: 1863–1892.Google Scholar

Koopmans, T., (1950), Statistical Inference in Dynamic Economic Models, Wiley, New York.

Krueger, A. (1999), “Experimental Estimates of Education Production Functions Experimental Estimates of Education Production Functions,” The Quarterly Journal ofEconomics, Vol. 114(2): 497–532.Google Scholar

Lalonde, R.J., (1986), “Evaluating the Econometric Evaluations of Training Programs with Experimental Data,” American Economic Review, Vol. 76: 604–620.Google Scholar

Lancaster, T. (2004), An Introduction to Modern Bayesian Econometrics, Blackwell Publishing.

Leamer, E. (1988), “Discussion on Marini, Singer, Glymour, Scheines, Spirtes, and Holland,” Sociological Methodology, Vol. 18: 485–493.Google Scholar

Lechner, M. (1999), “Earnings and Employment Effects of Continuous Off-the-job Training in East Germany After Unification,” Journal of Business and Economic Statistics, Vol. 17(1): 74–90.Google Scholar

Lechner, M. (2001), “Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional Independence Assumption,” in Lechner and Pfeiffer, eds. Econometric Evaluations of Active Labor Market Policies in Europe, Heidelberg.Google Scholar

Lechner, M. (2002), “Program Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labor Market Policies,” Review of Economics and Statistics, Vol. 84(2): 205–220.Google Scholar

Lechner, M. (2008), “A Note on the Common Support Problem in Applied Evaluation Studies,” Annales d'conomie et de Statistique, Vol. 91-92: 217–234.Google Scholar

Lee, D. (2008), “Randomized Experiments from Non-random Selection in U.S. House Elections,” Journal of Econometrics, Vol. 142(2): 675–697.Google Scholar

Lee, D., and T., Lemieux, (2010), “Regression Discontinuity Designs in Economics,” Journal of Economic Literature, Vol. 48(2): 281–355.Google Scholar

Lee, M.-J. (2005), Micro-Econometrics for Policy, Program, and Treatment Effects, Oxford University Press.

Lehman, E. (1974), Nonparametrics: Statistical Methods Based on Ranks, Holden-Day.

Lesaffre, E., and S., Senn, (2003), “A Note on Non-Parametric ANCOVA for Covariate Adjustment in Randomized Clinical Trials,” Statistics in Medicine, Vol. 22: 3583–3596.Google Scholar

Lin, W. (2012), “Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique,” Annals ofApplied Statistics.Google Scholar

Lindley, D. V., and N. R., Novick, (1981), “The Role of Exchangeability in Inference,” Annals of Statistics, Vol. 9: 45–58.Google Scholar

Little, R., and D., Rubin, (2002), Statistical Analysis with Missing Data, Wiley.

Lord, F. (1967), “A Paradox in the Interpretation of Group Comparisons,” Psychological Bulletin, Vol. 68: 304–305.Google Scholar

Lui, Kung-Jong (2011), Binary Data Analysis of Randomized Clinical Trials with Noncompliance, Wiley, Statistics in Practice.

Lynn, H., and C., McCulloch, (1992), “When Does It Pay to Break the Matches for Analysis of a Matched-pair Design,” Biometrics, Vol. 48: 397–409.Google Scholar

McCarthy, M. D. (1939), “On the Application of the z-Test to Randomized Blocks,” Annals of Mathematical Statistics, Vol. 10: 337.Google Scholar

McClellan, M., and J. P., Newhouse, (1994), “Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality,” Journal of the American Medical Association, Vol. 272(11): 859–866.Google Scholar

McDonald, C., S., Hiu, and W., Tierney, (1992), “Effects of Computer Reminders for Influenza Vaccination on Morbidity During Influenza Epidemics,” MD Computing, Vol. 9: 304–312.Google Scholar

McNamee, R. (2009), “Intention to Treat, Per Protocol, as Treated and Instrumental Variable Estimators Given Non-Compliance and Effect Heterogeneity,” Statistics in Medicine, Vol. 28: 2639–2652.Google Scholar

Mann, H. B., and D. R., Whitney, (1947), “On a Test of Whether One of Two Random Variables Is Stochastically Larger Than the Other,” Annals of Mathematical Statistics, Vol. 18(1): 50–60.Google Scholar

Manski, C. (1990), “Nonparametric Bounds on Treatment Effects,” American Economic Review Papers and Proceedings, Vol. 80: 319–323.Google Scholar

Manski, C. (1996), “Learning about Treatment Effects from Experiments with Random Assignment of Treatments,” The Journal of Human Resources, Vol. 31(4): 709–773.Google Scholar

Manski, C. (2003), Partial Identification of Probability Distributions, Springer-Verlag.

Manski, C. (2013), Public Policy in an Uncertain World, Harvard University Press.

Manski, C., G., Sandefur, S., McLanahan, and D., Powers, (1992), “Alternative Estimates of the Effect of Family Structure During Adolescence on High School,” Journal of the American Statistical Association, Vol. 87(417): 25–37.Google Scholar

Marini, M., and B., Singer, (1988), “Causality in the Social Sciences,” Sociological Methodology, Vol. 18: 347–409.Google Scholar

Mealli, F., and D., Rubin, (2002a), “Assumptions When Analyzing Randomized Experiments with Noncompliance and Missing Outcomes,” Health Services Outcome Research Methodology, Vol. 3: 225–232.Google Scholar

Mealli, F., and D., Rubin, (2002b), “Discussion of Estimation of Intervention Effects with Noncom-pliance: Alternative Model Specification by Booil Jo,” Journal of Educational and Behavioral Statistics, Vol. 27: 411–415.Google Scholar

Meier, P. (1991), “Compliance as an Explanatory Variable in Clinical Trials: Comment,” Journal of the American Statistical Association, Vol. 86(413): 19–22.Google Scholar

Miguel, E., C., Camerer, k., Casey j., Cohen, K. M., Esterling A., Gerber R., Glennerster D. P., Green M., Humphreys, G., Imbens, D., Laitin, T., Madon, L., Nelson B. A, Nosek, M., Petersen, R., Sedlmayr, J. P., Simmons, U., Simonsohn, and M., Van der Laan, (1991), “Promoting Transparency in Social Science Research,” Science, Vol. 343(6166): 30–31.Google Scholar

Mill, J. S. (1973), A system of logic, In Collected Works of John Stuart Mill, University of Toronto Press.

Miratrix, L., J., Sekhon, and B., Yu, (2013), “Ajdusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments,” Journal of the Royal Statistical Society, Series B, 75, 369–396.Google Scholar

Morgan, K., and D., Rubin, (2012), “Rerandomization to Improve Covariate Balance in Experiments,” Annals of Statistics, Vol. 40(2): 1263–1282.Google Scholar

Morgan, S. (2013), Handbook of Causal Analysis for Social Research,Springer.

Morgan, S., and C., Winship, (2007), Counterfactuals and Causal Inference, Cambridge University Press.

Morris, C., and J., Hill, (2000), “The Health Insurance Experiment: Design Using the Finite Selection Model,” Public Policy and Statistics: Case Studies from RAND 2953. Springer, New York.

Morton, R., and K., Williams, (2010), Experimental Political Science and the Study of Causality, Cambridge University Press.

Mosteller, F. (1995), “The Tennessee Study of Class Size in the Early School Grades,” The Future of Children: Critical Issues for Children and Youths, V(1995): 113–127.Google Scholar

Murnane, R., and J., Willett, (2011), Methods Matter: Improving Causal Inference in Educational and Social Science Research, Oxford University Press.

Murphy, D., and L., Cluff, (1990), “SUPPORT: Study to understand prognoses and preferences for outcomes and risks of treatmentsstudy design,” Journal of Clinical Epidemiology, Vol. 43: 1S-123S.Google Scholar

Neyman, J. (1923, 1990), “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9,” translated in Statistical Science, (with discussion), Vol. 5(4): 465–480, 1990.Google Scholar

Neyman, J. (1934), “On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection,” Journal of the Royal Statistical Society, Vol. 97(4): 558–625.Google Scholar

Neyman, J. with the cooperation of K., Iwaskiewicz and St., Kolodziejczyk, (1935), “Statistical Problems in Agricultural Experimentation,” (with discussion), Supplement, Journal ofthe Royal Statistal Society, Series B, Vol. 2: 107–180.Google Scholar

Pattanayak, C., D., Rubin, and E., Zell, (2011), “Propensity Score Methods for Creating Covariate Balance in Observational Studies,” Review ofExperimental Cardiology, Vol. 64(10): 897–903.Google Scholar

Paul, I., J., Beiler, A., McMonagle, M., Shaffer, L., Duda, and C., Berlin, (2007), “Effect of Honey, Dex-tromerhorphan, and No Treatment on Nocturnal Cough and Sleep Quality for Coughing Childre and Their Parents,” Archives of of Pediatric and Adolescent Medicine, Vol. 161(12): 1140–1146.Google Scholar

Pearl, J. (1995), “Causal Diagrams for Empirical Research,” Biometrika, Vol. 82: 669–688.Google Scholar

Pearl, J., (2000, 2009), Causality: Models, Reasoning and Inference, Cambridge University Press.

Peirce, C., and J., Jastrow, (1885), “On Small Differences in Sensation,” Memoirs of the National Academy of Sciences, Vol.3: 73–83.Google Scholar

Peters, C., and W., van Vorhis, (1941), Statistical Procedures and Their Mathematical Bases, McGraw-Hill.

Politis, D., and J., Romano, (1999), Subsampling, Springer Verlag.

Porter, J. (2003), “Estimation in the Regression Discontinuity Model,” Unpublished Manuscript, Harvard University.

Powers, D., and S., Swinton, (1984), “Effects of Self-Study for Coachable Test Item Types,” Journal of Educational Measurement, Vol. 76: 266–278.Google Scholar

Quade, D. (1982), “Nonparametric Analysis of Covariance by Matching,” Biometrics, Vol.38: 597–611.Google Scholar

Reid, C. (1998), Neyman from Life, Springer.

Reinisch, J., S., Sanders, E., Mortensen, and D., Rubin, (1995), “In Utero Exposure to Phenobarbital and Intelligence Deficits in Adult Men,” The Journal of the American Medical Association, Vol. 274(19): 1518–1525.Google Scholar

Robert, C. (1994), The Bayesian Choice, Springer Verlag.

Robert, C., and G., Casella, (2004), Monte Carlo Statistical Methods, Springer Verlag.

Robins, J. (1986), “A New Approach to Causal Inference in Mortality Studies with Sustained Exposure Periods - Application to Control of the Healthy Worker Survivor Effect,” Mathematical Modelling, Vol.7: 1393–1512.Google Scholar

Robins, J., and Y., Ritov, (1997), “Towards a Curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semi-parametric Models,” Statistics in Medicine, Vol. 16: 285–319.Google Scholar

Robins, J. M., and A., Rotnitzky, (1995), “Semiparametric Efficiency in Multivariate Regression Models with Missing Data,” Journal ofthe American Statistical Association, Vol. 90: 122–129.Google Scholar

Robins, J. M., Rotnitzky, A., and Zhao, L-P., (1995), “Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journal of the American Statistical Association, Vol. 90: 106–121.Google Scholar

Romer, C. D., and D. H., Romer, (2004), “A New Measure of Monetary Shocks: Derivation and Implications,” The American Economic Review, Vol. 94(4): 1055–1084.Google Scholar

Rosenbaum, P. (1984a), “Conditional Permutation Tests and the Propensity Score in Observational Studies,” Journal of the American Statistical Association, Vol. 79: 565–574.Google Scholar

Rosenbaum, P. (1984b), “The Consequences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment,” Journal of the Royal Statistical Society, Series A, Vol. 147: 656–666.Google Scholar

Rosenbaum, P. (1987), “The Role of a Second Control Group in an Observational Study,” Statistical Science, (with discussion), Vol. 2(3), 292–316.Google Scholar

Rosenbaum, P. (1988), “Permutation Tests for Matched Pairs,” Applied Statistics, Vol. 37: 401–411.Google Scholar

Rosenbaum, P. (1989a), “Optimal Matching in Observational Studies,” Journal of the American Statistical Association, 84, 1024–1032.Google Scholar

Rosenbaum, P. (1989b), “On Permutation Tests for Hidden Biases in Observational Studies: An Application of Holley's Inequality to the Savage Lattice,” Annals of Statistics, Vol. 17: 643–653.Google Scholar

Rosenbaum, P. (1995, 2002), Observational Studies, Springer Verlag.

Rosenbaum, P. (2009), Design of Observational Studies, Springer Verlag.

Rosenbaum, P. (2002), “Covariance Adjustment in Randomized Experiments and Observational Studies,” Statistical Science, Vol. 17(3): 286–304.Google Scholar

Rosenbaum, P., and D., Rubin, 1983a), “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, Vol. 70: 41–55.Google Scholar

Rosenbaum, P., and D., Rubin, (1983b), “Assessing the Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome,” Journal ofthe Royal Statistical Society, SeriesB, Vol. 45: 212–218.Google Scholar

Rosenbaum, P., and D., Rubin, (1984), “Reducing the Bias in Observational Studies Using Sub-classification on the Propensity Score,” Journal of the American Statistical Association, Vol. 79: 516–524.Google Scholar

Rosenbaum, P., and D., Rubin, (1985), “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score,” American Statistician, Vol. 39: 33–38.Google Scholar

Rubin, D. B. 1973a), “Matching to Remove Bias in Observational Studies,” Biometrics, Vol. 29: 159–183.Google Scholar

Rubin, D. B. (1973b), “The Use of Matched Sampling and Regression Adjustments to Remove Bias in Observational Studies,” Biometrics, Vol. 29: 185–203.Google Scholar

Rubin, D. B. (1974), “Estimating Causal Effects of Treatments in Randomized and Non-randomized Studies,” Journal of Educational Psychology, Vol. 66: 688–701.Google Scholar

Rubin, D. B. (1975), “Bayesian Inference for Causality: The Importance of Randomization,” Proceedings of the Social Statistics Section of the American Statistical Association, 233–239.Google Scholar

Rubin, D. B. 1976a), “Multivariate Matching Methods That Are Equal Percent Bias Reducing, I: Some Examples,” Biometrics, Vol. 32(1): 109–120.Google Scholar

Rubin, D. B. (1976b), “Multivariate Matching Methods That Are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample Sizes,” Biometrics, Vol. 32(1): 121–132.Google Scholar

Rubin, D. B. (1976c), “Inference and Missing Data,” Biometrika, (with discussion and reply), Vol. 63(3): 581–592.Google Scholar

Rubin, D. B. (1977), “Assignment to Treatment Group on the Basis of a Covariate,” Journal of Educational Statistics, Vol. 2(1): 1–26.Google Scholar

Rubin, D. B. (1978), “Bayesian Inference for Causal Effects: The Role of Randomization,” Annals of Statistics, Vol. 6: 34–58.Google Scholar

Rubin, D. B. (1979), “Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies,” Journal of the American Statistical Association, Vol. 74: 318–328.Google Scholar

Rubin, D. B. 1980a), “Discussion” of “Randomization Analysis of Experimental Data in the Fisher Randomization Test” by “Basu,” The Journal ofthe American Statistical Association, Vol. 75(371): 591–593.Google Scholar

Rubin, D. B. (1980b), “Bias Reduction Using Mahalanobis' Metric Matching,” Biometrics, Vol. 36(2): 293–298.Google Scholar

Rubin, D. B. 1986a), “Statistics and Causal Inference: Comment: Which Ifs Have Causal Answers,” Journal ofthe American Statistical Association, Vol. 81(396): 961–962.Google Scholar

Rubin, D. B. (1986b), “Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations,” Journal of Business and Economic Statistics, Vol. 4(1): 87–94.Google Scholar

Rubin, D. B. 1990a), “Formal Modes of Statistical Inference for Causal Effects,” Journal of Statistical Planning and Inference, Vol.25 : 279–292.Google Scholar

Rubin, D. B. (1990b), “Comment on Neyman (1923) and Causal Inference in Experiments and Observational Studies,” Statistical Science, Vol. 5(4): 472–480.Google Scholar

Rubin, D. B. (1998), “More Powerful Randomization-Based p-Values in Double-Blind Trials with Non-Compliance,” Statistics in Medicine, Vol. 17: 371–385.Google Scholar

Rubin, D. B. (2001), “Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation,” Health Services & Outcomes Research Methodology, Vol. 2: 169–188.Google Scholar

Rubin, D. B. (2004), “Causal Inference Using Potential Outcomes: Design, Modeling, Decisions,” Fisher Lecture, The Journal of the American Statistical Association, Vol. 100(469): 322–331.Google Scholar

Rubin, D. B. (2005).

Rubin, D. B. (2006), Matched Sampling for Causal Effects, Cambridge University Press.

Rubin, D. B. (2007), “The Design versus the Analysis of Observational Studies for Causal Effects: Parallels with the Design of Randomized Trials,” Statistics in Medicine, Vol. 26(1): 20–30.Google Scholar

Rubin, D. B. (2008), “The Design and Analysis of Gold Standard Randomized Experiments. Comment on ‘Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random to Nonrandom Assignment’ by Shadish, Clark, and Steiner,” Journal of the American Statistical Association, Vol. 103: 1350–1353.Google Scholar

Rubin, D. B. (2010), “Reflections Stimulated by the Comments of Shadish (2010) and West and Thoemmes (2010),” Psychological Methods, Vol. 15(1): 38–46.Google Scholar

Rubin, D. B. (2012), “Analyses That Inform Policy Decisions,” Biometrics, Vol. 68: 671–775.Google Scholar

Rubin, D., and E., Stuart, (2006), “Affinely Invariant Matching Methods with Discriminant Mixtures of Ellipsoidally Symmetric Distributions,” Annals of Statistics, Vol. 34(4): 1814–1826.Google Scholar

Rubin, D. B., and N., Thomas, (1992a), “Affinely Invariant Matching Methods with Ellipsoidal Distributions,” Annals of Statistics, Vol. 20(2): 1079–1093.Google Scholar

Rubin, D. B., and N., Thomas, (1992b), “Characterizing the Effect of Matching Using Linear Propensity Score Methods with Normal Distributions,” Biometrika, Vol. 79(4): 797–809.Google Scholar

Rubin, D. B., and N., Thomas, (1996), “Matching Using Estimated Propensity Scores: Relating Theory to Practice,” Biometrics 52: 249–264.Google Scholar

Rubin, D. B., and N., Thomas, (2000), “Combining Propensity Score Matching with Additional Adjustment for Prognostic Covariates,” Journal of the American Statistical Association, Vol. 95(450): 573–585.Google Scholar

Rubin, D. B., X., Wang, L., Yin, and E., Zell, (2010), “Bayesian Causal Inference: Approaches to Estimating the Effect of Treating Hospital Type on Cancer Survival in Sweden Using Principal Stratification,” in A., OHagen and M., West, eds. The Handbook of Applied Bayesian Analysis, Chapter 24, pp. 679–706.Google Scholar

Rubin, D. B., and E., Zell, (2010), “Dealing with Noncompliance and Missing Outcomes in a Ran-domized Trial using Bayesian Technology: Prevention of Perinatal Sepsis Clinical Trial, Soweto, South Africa,” Statistical Methodology, Vol. 7(3): 338–350.Google Scholar

Sabbaghi, A., and D., Rubin, (2014), “Comments on the Neyman-Fisher Controversy and Its Consequences,” Statistical Science, Vol. 29(2): 267–284.Google Scholar

Samii, C., and P., Aronow, (2012), “Equivalencies Between Design-Based and Regression-Based Variance Estimators for Randomized Experiments,” Statistics and Probability Letters, Vol. 82: 365–370.Google Scholar

Sekhon, J. (2004-2013), “Matching: Multivariate and Propensity Score Matching with Balance Optimization,” http://sekhon.berkeley.edu/matching, http://cran.r-project.org/package=Matching.Google Scholar

Senn, S. (1994), “Testing for Baseline Balance in Clinical Trials,” Statistics in Medicine, Vol. 13: 1715–1726.Google Scholar

Shadish, W., T., Campbell, and T., Cook, (2002), Experimental and Quasi-experimental Designs for Generalized Causal Inference, Houghton and Mifflin.

Sheiner, L., and D., Rubin, (1995), “Intention-to-treat Analysis and the Goals of Clinical Trial,” Clinical Pharmacology and Therapeutics, Vol. 57: 6–15.Google Scholar

Shipley, M., P., Smith, and M., Dramaix, (1989), “Calculation of Power for Matched Pair Studies when Randomization is by Group,” International Journal of Epidemiology, Vol. 18(2): 457–461.Google Scholar

Shu, Y., G., Imbens Z., Cui D.F. and Z., Kadziola, (2013), “Propensity Score Matching and Subclassification with Multivalued Treatments,” Unpublished Manuscript.

Sianesi, B. (2001), “Psmatch: Propensity Score Matching in STATA,” University College London, and Institute for Fiscal Studies.

Sims, C. (1972), “Money, Income and Causality,” American Economic Review, Vol. 62(4): 540–552.Google Scholar

Smith, J. A., and P. E., Todd, (2001), “Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods,” American Economic Review, Papers and Proceedings, Vol. 91: 112–118.Google Scholar

Smith, J. A., and P. E., Todd, (2005), “Does Matching Address LaLonde's Critique of Nonexperimental Estimators,” Journal of Econometrics, Vol. 125(12): 305–353.Google Scholar

Snedecor, G., and W., Cochran, (1967, 1989), Statistical Methods, Iowa State University Press.

Sommer, A., I., Tarwotjo, E., Djunaedi, K., West, A., Loeden, R., Tilden, and L., Mele, (1986), “Impact of Vitamin A Supplementation on Child Mortality: A Randomized Controlled Community Trial,” Lancet, Vol. 1: 1169–1173.Google Scholar

Sommer, A., and S., Zeger, (1991), “On Estimating Efficacy from Clinical Trials,” Statistics in Medicine, Vol. 10: 45–52.Google Scholar

Stigler, S. (1986), American Contributions to Mathematical Statistics in the Nineteenth Century, Arno Press.

Stock, J., and F., Trebbi, (2003), ”Who Invented Instrumental Variable Regression?” Journal of Economic Perspectives, Vol. 17: 177–194.Google Scholar

”Student” (1923), “On Testing Varieties of Cereals,” Biometrika, Vol. 15: 271–293.

Tanner, M. (1996), Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, Springer Verlag.

Tanner, M., and W., Wong, (1987), ”The Calculation of Posterior Distributions by Data Augmentation,” Journal of the American Statistical Association, Vol. 82(398): 528–540.Google Scholar

Thistlewaite, D., and D., Campbell, (1960), “Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment,” Journal ofEducational Psychology, Vol. 51: 309–317.Google Scholar

Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B(Methodological), Vol. 58(1): 267–288.Google Scholar

Tinbergen, J. (1930), “Bestimmung und Deutung von Angebotskurven: in Beispiel,” Zietschrift fur Nationalokonomie, 669–679.Google Scholar

Torgerson, D., and M., Roland, (1998), “Understanding Controlled Trials: What Is Zelen's Design?” BMJ, Vol. 316:606.Google Scholar

Van Der Klaauw, W. (2002), “A Regression-discontinuity Evaluation of the Effect of Financial Aid Offers on College Enrollment,” International Economic Review, Vol. 43(4): 1249–1287.Google Scholar

Van Der Laan, M., and J., Robins, (2003), Unified Methods for Censored Longitudinal Data and Causality, Springer Verlag.

Van Der Vaart, A., (1998), Asymptotic Statistics, Cambridge University Press, Cambridge.

Victora, C., J.-P., Habicht, and J., Bryce, (2004), “Evidence-Based Public Health: Moving Beyond Randomized Trials,” American Journal of Public Health, Vol. 94(3): 400–405.Google Scholar

Waernbaum, I. (2010), “Model Misspecification and Robustness in Causal Inference: Comparing Matching with Doubly Robust Estimation,” Statistics in Medicine, Vol. 31(15): 1572–1581.Google Scholar

Welch, B. (1937), “On the z Test in Randomized Blocks and Latin Squares,” Biometrika, Vol. 29: 21–52.Google Scholar

Wilcoxon, F. (1945), “Individual Comparisons by Ranking Methods,” Biometrics Bulletin, Vol. 1(6): 80–83.Google Scholar

Wooldridge, J. (2002), Econometric Analysis of Cross Section and Panel Data, 2nd edition, MIT Press.

Wright, P. (1928), The Tariffon Animal and Vegetable Oils, Macmillan.

Wright, S. (1921), “Correlation and Causation,” Journal of Agricultural Research, Vol. 20: 257–285.Google Scholar

Wright, S. (1923), “The Theory of Path Coefficients: A Reply to Niles' Criticism,” Genetics, Vol.8: 239–255.Google Scholar

Wu, J., and Hamada, M. (2009), Experiments, Planning, Analysis and Optimization, Wiley Series in Probability and Statistics.

Yang, S., G., Imbens, Z., Cui, D., Faries, and Z., Kadziola, (2014) “Propensity Score Matching and Subclassification with Multi-level Treatments,” unpublished manuscript.

Yule, G. N. (1897), “On the Theory of Correlation,” Journal of the Royal Statistical Society, 812–854.Google Scholar

Zelen, M. (1979), “A New Design for Randomized Clinical Trials,” New England Journal of Medicine, Vol. 300: 1242–1245.Google Scholar

Zelen, M. (1990), “Randomized Consent Designs for Clinical Trials: An Update,” Statistics in Medicine, Vol. 9: 645–656.Google Scholar

Zhao, Z. (2004), “Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics and an Application,” Review of Economics and Statistics, Vol. 86(1): 91–107.Google Scholar

Zhang, J., D., Rubin, and F., Mealli, (2009), “Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification,” Journal of the American Statistical Association, Vol. 104(485): 166–176.Google Scholar