2,585
Views
17
CrossRef citations to date
0
Altmetric
Review Articles

Essential concepts of causal inference: a remarkable history and an intriguing future

Pages 140-155 | Received 27 Jun 2019, Accepted 18 Sep 2019, Published online: 29 Sep 2019

References

  • Jaynes J. The origin of consciousness in the break-down of the bicameral mind. Boston (MA): Houghton Mifflin Company; 1976.
  • Box GEP. Science and statistics. J Am Stat Assoc. 1976;71(356):791–799.
  • Von Neumann J. The mathematician. Haywood (RB): works of the mind. Chicago (IL): University of Chicago Press; 1947; p. 180–196.
  • Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhya A. 1973;35(4):417–446.
  • Rubin DB. Comment: Neyman (1923) and causal inference in experiments and observational studies. Stat Sci. 1990;5(4):472–480.
  • Fisher RA. Statistical methods for research workers (first edition). Cambridge: Oliver and Boyd; 1925.
  • Fisher RA. Design of experiments. Edinburgh, London: Oliver and Boyd; 1935.
  • Fisher RA. The causes of human variability. Eugen Rev. 1918;10:213–220.
  • Rubin DB. Comment on “Randomization analysis of experimental data in the Fisher randomization test” by Basu. J Am Stat Assoc. 1980;75(371):591–593.
  • Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc. 2005;100(469):322–331.
  • Neyman JS. On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Roczniki Nauk Rolniczych Tom X (in Polish) (1923); Translated in Statistical Science. 1991;5:465–480.
  • Reid C. Neyman-from life. New York (NY): Springer; 1982.
  • Neyman JS. On the Application of probability theory to agricultural experiments: essay on principles, Section 9. Stat Sci. 1990;5(4):465–472.
  • Speed TP. Introductory remarks on Neyman (1923). Stat Sci. 1990;5(4):463–464.
  • Neyman JS. Statistical problems in agricultural experimentation (with discussion). Suppl J Royal Stat Soc Ser B. 1935;2:107–180.
  • Kempthorne O. The design and analysis of experiments. Oxford: Wiley; 1952.
  • Cox DR. Planning of experiments. Oxford: Wiley; 1958.
  • Imai K, Tingley D, Yamamoto T. Experimental designs for identifying causal mechanisms. J Royal Stat Soc A. 2013;176(1):5–51.
  • Mealli F. Discussion on the paper “Experimental designs for identifying causal mechanisms” by Imai, Tingley and Yamamoto. J Royal Stat Soc A. 2013;176(1):32–33.
  • Rubin DB. Discussion of “experimental designs for identifying causal mechanisms” by Imai. Tingley and Yamamoto. J Royal Stat Soc A. 2013;176(1):45.
  • Rubin DB. Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). Pshchol. Methods. 2010;15(1):38–46.
  • Neyman JS. On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. J Royal Stat Soc. 1934;97(4):558–625.
  • Hacking I. The emergence of probability. Cambridge: Cambridge University Press; 1975.
  • Fisher RA, Machenzie WA. Studies in crop variation. II. The manorial response of different potato varieties. J Agr Sci. 1923;13:311–320.
  • Box JF. R.A. Fisher the life of a scientist. New York (NY): Wiley; 1978.
  • Cochran WG, Cox GM. Experimental design. New York (NY): Wiley; 1957.
  • Box GEP, Hunter WG, Hunter JS. Statistics for experiments. New York (NY): Wiley; 1978.
  • Peirce CS, Jastrow J. On small differences in sensation. Mem Natl Acad Sci. 1885;3:73–83.
  • Yule GU. An investigation into the causes of changes in Pauperism in England, chiefly during the last two intercensal decades (part I.). J Royal Stat Soc. 1899;62(2):249–255.
  • SAS [Internet]. Cary (NC): SAS 2019 Institute Inc; [cited 2019 Jun 18]. Available from: https://www.sas.com/en_us/home.html.
  • US department of health, education, and welfare. Smoking and health, report of the advisory committee to the surgeon general of the public health service. Washington (DC): Public Health Service No.1103; 1964.
  • Cornfield J. Principles of research. Am J Ment Defic. 1959;64:240–252.
  • Holland PW. Statistics and causal inference. J AmStat Assoc. 1986;81(396):945–960.
  • Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66(5):688–701.
  • Rubin DB. Bayesian inference for causality: the importance of randomization. Proc Soc Stat Sect Am Stat Assoc. 1975;233–239.
  • Rubin DB. Inference and missing data. Biometrika. 1976;63(3):581–592.
  • Rubin DB. Assignment to treatment group on the basis of a covariate. J Educ Stat. 1977;2(1):1–26.
  • Rubin DB. Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978;6(1):34–58.
  • Blalock HM. Causal models in the social sciences. New York (NY): Routledge; 1985.
  • Rubin DB. The practical importance of understanding placebo effects and their role when approving drugs and recommending dosages. Behaviormetrika. 2019. to appear.
  • Rubin DB. A case-study of the robustness of Bayesian methods of inference: estimating the total in a finite population using transformations to normality. In: Scientific inference, data analysis and Robustness. New York: Academic Press, Inc; 1983. p. 213–244.
  • Hocking RR, Smith WB. Estimation of parameters in the multivariate normal distribution with missing observations. J Am Stat Assoc. 1968;63:159–173.
  • Trawinski IM, Bargmann RE. Maximum likelihood estimation with incomplete multivariate data. Ann Math Stat. 1964;35(2):647–657.
  • Cochran WG. Sampling techniques. New York (NY): Wiley; 1963.
  • Savage LJ. The foundations of statistical inference. New York (NY): Wiley; 1962.
  • Ericson WA. Subjective Bayesian models in sampling finite populations. J Royal Stat Soc Series B. 1969;31(2):195–233.
  • Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–455.
  • Imbens G, Rubin DB. Causal inference for statistics, social, and biomedical sciences: an introduction. New York (NY): Cambridge University Press; 2015.
  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.
  • Rosenbaum PR, Rubin DB. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J Royal Stat Soc B. 1983;45(2):212–218.
  • Rosenbaum PR, Rubin DB. Estimating the effects caused by treatments.” discussion of “On the nature and discovery of structure. J Am Stat Assoc. 1984;79:26–28.
  • Rosenbaum PR, Rubin DB. Sensitivity of Bayes inference with data-dependent stopping rules. Am Stat. 1984;38:106–109.
  • Rosenbaum PR, Rubin DB. Difficulties with the regression analysis of age–adjusted rates. Biometrics. 1984;40:437–443.
  • Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79:516–524.
  • Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling incorporating the propensity score. Am Stat. 1985;39:33–38.
  • Rosenbaum PR, Rubin DB. The bias due to incomplete matching. Biometrics. 1985;41:103–116.
  • Rosenbaum PR, Rubin DB. A difficulty with regression analysis of regional test–score averages. discussion of on state education statistics. J Educ Stat. 1985;10(4):326–333.
  • Rosenbaum PR. The role of a second control group in an observational study. Statist Sci. 1987;2(3):292–306.
  • Rosenbaum PR. Overt bias in observational studies. New York (NY): Springer; 2002.
  • Rosenbaum PR. Heterogeneity and causality: unit heterogeneity and design sensitivity in observational studies. The Am. Stat. 2005;59:147–152.
  • Rosenbaum PR. Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies. Biometrics. 2007;63(2):456–464.
  • Rosenbaum PR. Design of observational studies. New York (NY): Springer; 2010.
  • Haviland A, Nagin DS, Rosenbaum PR. Combining propensity score matching and group-based trajectory analysis in an observational study. Psychol Methods. 2007;12(3):247–267.
  • Heller R, Rosenbaum PR, Small DS. Split samples and design sensitivity in observational studies. J Am Stat Assoc. 2009;104:1090–1101.
  • Rosenbaum PR, Krieger AM. Sensitivity of two-sample permutation inferences in observational studies. J Am Stat Assoc. 1990;85:493–498.
  • Pearl J, Mackenzie D. The book of why: the new science of cause and effect. New York (NY): Basic Books; 2018.
  • Van der Laan MJ, Robins JM. Unified methods for censored longitudinal data and causality. New York (NY): Springer; 2003.
  • Hirano K, Imbens G, Rubin DB, et al. Assessing the effect of an influenza vaccine in an encouragement design. Biostatistics. 2000;1:69–88.
  • Chen H, Geng Z, Zhou XH. Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data (with discussion). Biometrics. 2009;65:675–682.
  • Taylor L, Zhou XH. Methods for clustered encouragement design studies with noncompliance and missing data. Biostatistics. 2010;12:313–326.
  • Wang L, Richardson T, Zhou XH. Causal analysis in multi-arm trials with truncation by death. J Royal Stat Soc B. 2017;79:719–735.
  • Wang L, Zhou XH, Richardson TS. Identification and estimation of causal effects with outcomes Truncated by Death. Biometrika. 2017;104:597–612.
  • Zhou XH, Li SM. ITT analysis of randomized encouragement design studies with missing data. Stat Med. 2006;25:2737–2761.
  • Morgan KL, Rubin DB. Rerandomization to improve covariate balance in experiments. Ann Stat. 2012;40(2):1263–1282.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.