• 1
    GelmanA, MengXL (eds), In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. Wiley: Chichester, 2004.
  • 2
    Holland PM. Statistics and causal inference. Journal of the American Statistical Association 1986; 81(39):945960.
  • 3
    Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66(5):688701.
  • 4
    Rubin DB. Bayesian inference for causality: the importance of randomization. The Proceedings of the Social Statistics Section of the American Statistical Association. American Statistical Association: Alexandria, VA, 1975; 233239.
  • 5
    Rubin DB. Inference and missing data. Biometrika 1976; 63(3):581592 (with Discussion and Reply).
  • 6
    Rubin DB. Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics 1977; 2(1):126. Printer's correction note 3, 384.
  • 7
    Rubin DB. Bayesian inference for causal effects: the role of randomization. Annals of Statistics 1978; 6(1):3458.
  • 8
    Rubin DB. Discussion of ‘conditional independence in statistical theory’, by Dawid AP. Journal of the Royal Statistical Society, Series B 1979; 41(1):2728.
  • 9
    Rubin DB. Discussion of ‘randomization analysis of experimental data in the Fisher randomization test’ by Basu. Journal of the American Statistical Association 1980; 75(371):591593.
  • 10
    Rubin DB. Neyman (1923) and causal inference in experiments and observational studies. Statistical Science 1990; 5(4):472480.
  • 11
    Imbens G, Rubin DB. Causal Inference in Statistics, and in the Social and Biomedical Sciences. Cambridge University Press: New York, 2006, in press.
  • 12
    Neyman J. On the application of probability theory to agricultural experiments: essay on principles, Section 9. Annals of Agricultural Science 1923; Translated in Statistical Science 1990; 5(4):465–472.
  • 13
    Imbens G, Rubin DB. Rubin causal model. The New Palgrave Dictionary of Economics (2nd edn). Palgrave McMillan: New York, 2006, in press.
  • 14
    Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. 2004 Fisher Lecture. The Journal of the American Statistical Association 2005; 100(469):322331.
  • 15
    Holland PW, Rubin DB. On Lord's paradox. Principles of Modern Psychological Measurement: A Festschrift for Frederick Lord. Erlbaum: Hillsdale, NJ, 1983; 325.
  • 16
    Fisher RA. Statistical Methods for Research Workers. Oliver and Boyd: Edinburgh, 1925.
  • 17
    Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:4155.
  • 18
    Roy AD. Some thoughts on the distribution of earnings. Oxford Economic Papers 1951; 3:135146.
  • 19
    Haavelmo T. The probability approach in econometrics. Econometrica 1944; 15:413419.
  • 20
    Imbens G, Rubin DB. Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics 1997; 25(1):305327.
  • 21
    Rubin DB, Stuart EA, Zanutto EL. A potential outcomes view of value-added assessment in education. Journal of Educational and Behavioral Statistics 2004; 29(1):103116.
  • 22
    McCaffrey DF, Stuart EA, Rubin DB, Zanutto E. Design and implementation of case–control matching to estimate the effects of value-added assessment. Unpublished Paper, Rand Corporation, 2006.
  • 23
    Langenskiöld S. Peer influence on smoking: causation or correlation? Ph.D. Thesis, Stockholm School of Economics, Stockholm, 2005.
  • 24
    Rubin DB. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Services and Outcomes Research Methodology 2002; 2:169188.
  • 25
    Rubin DB. Statistical issues in the estimation of the causal effects of smoking due to the conduct of the tobacco industry. In Statistical Science in the Courtroom (Chapter 16), GastwirthJ (ed.). Springer: New York, 2000; 321351.
  • 26
    Rubin DB. Estimating the causal effects of smoking. Statistics in Medicine 2001; 20:13951414.
  • 27
    Rubin DB. The ethics of consulting for the tobacco industry. Special issue on ‘Ethics, Statistics and Statisticians’. Statistical Methods in Medical Research 2002; 11(5):373380.
  • 28
    Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhya—A 1973; 35(4):417446.
  • 29
    Cochran WG. The planning of observational studies of human populations (with Discussion). Journal of the Royal Statistical Society, Series A 1965; 128:234266.
  • 30
    Rubin DB. Matching to remove bias in observational studies. Biometrics 1973; 29(1):159183. Printer's correction note 30, 728.
  • 31
    Rubin DB. The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics 1973; 29(1):184203.
  • 32
    Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling incorporating the propensity score. The American Statistician 1985; 39:3338.
  • 33
    Rubin DB, Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association 2000; 95(450):573585.
  • 34
    Rubin DB, Thomas N. Affinely invariant matching methods with ellipsoidal distributions. Annals of Statistics 1992; 20(2):10791093.
  • 35
    Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968; 24:295313.
  • 36
    Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516524.
  • 37
    Shadish WR, Clark MH. A randomized experiment comparing random to nonrandom assignment. Unpublished Paper, University of California, Merced, 2006.