Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by “sophisticated” models. This article discusses current models for causation, as applied to experimental and observational data. The intention-totreat principle and the effect of treatment on the treated will also be discussed. Flaws in perprotocol and treatment-received estimates will be demonstrated.
Abbott A.1997. Of time and space: The contemporary relevance of the Chicago school. Social Forces75:1149–1182.
2.
Abbott A.1998. The causal devolution. Sociological Methods & Research27:148–181.
3.
Angrist J. D.G. W. Imbens and D. B. Rubin1996. Identification of causal effects using instrumental variables. Journal of the American Statistical Association91:444–472.
4.
Angrist J. D. and A. B. Krueger. 2001. Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives19:2–16.
5.
Arceneaux K.A. S. Gerber, and D. P. Green. 2006. Comparing experimental and matching methods using a large-scale voter mobilization experiment. Political Analysis14:37–62.
6.
Berk, R. A.2004. Regression analysis: A constructive critique. Thousand Oaks, CA: Sage.
7.
Bloom, H. S.1984. Accounting for no-shows in experimental evaluation designs. Evaluation Review8:225–246.
8.
Brady, H. E., and D. Collier, eds. 2004. Rethinking social inquiry: Diverse tools, shared standards. Lanham, MD: Rowman&Littlefield.
9.
Dabrowska, D., and T. P. Speed. 1990. On the application of probability theory to agricultural experiments. Essay on principles. English translation of Neyman (1923). Statistical Science5:463–480 (with discussion).
10.
Evans, A. S.1993. Causation and disease: A chronological journey. New York: Plenum.
11.
Freedman, D. A.2005a. Linear statistical models for causation: A critical review. In Encyclopedia of statistics in behavioral science, ed. B. S. Everitt and D. C. Howell. Chichester, UK: John Wiley.
12.
Freedman, D. A.2005b. Statistical models: Theory and practice. New York: Cambridge University Press.
13.
Freedman, D. A.2006. On regression adjustments to experimental data. Technical report, Statistics Department, University of California, Berkeley. http://www.stat.berkeley.edu/users/census/neyreg.pdf.
14.
Freedman, D. A., D. B. Petitti, and J. M. Robins. 2004. On the efficacy of screening for breast cancer. International Journal of Epidemiology33:43–73 (with discussion). Correspondence, pp. 1404–6.
15.
Hart, H. L. A., and A. M. Honoré. 1985. Causation in the law. 2nd ed.Oxford: Oxford University Press.
16.
Heckman, J. J.2000. Causal parameters and policy analysis in economics: A twentieth century retrospective. Quarterly Journal of Economics115:45–97.
17.
Heckman, J., and R. Robb. 1985. Alternative methods for estimating the impact of interventions. In Longitudinal analysis of labor market data, ed. J. Heckman and B. Singer, 156–245. New York: Cambridge University Press.
18.
Heckman, J., J. L. Tobias, and E. Vytlacil. 2001. Four parameters of interest in the evaluation of social programs. Southern Economic Journal68:210–223.
19.
Hedström, P., and R. Swedberg, eds. 1998. Social mechanisms. Cambridge: Cambridge University Press.
20.
Hill, A. B.1961. Principles of medical statistics. 7th ed.London: The Lancet.
21.
Hodges, J. L., Jr., and E. Lehmann. 1964. Basic concepts of probability and statistics. San Francisco: Holden-Day.
22.
Holland, P. W.1986. Statistics and causal inference. Journal of the American Statistical Association8:945–970 (with discussion).
23.
Holland, P. W.1988. Causal inference, path analysis, and recursive structural equation models. In Sociological methodology 1988, ed. C. Clogg, chap. 13. Washington, DC: American Sociological Association.
24.
Hume, D.1748. Philosophical essays concerning human understanding. London: A. Millar. Retitled An enquiry concerning human understanding in 1758. Widely reprinted, e.g., by Oxford University Press, 2005, ed. T. L. Beauchamp.
25.
Imbens, G., and J. Angrist. 1994. Identification and estimation of local average treatment effects. Econometrica62:467–475.
26.
Lewis, D.1973. Causation. Journal of Philosophy70:556–567.
27.
Lieberson, S.1985. Making it count. Berkeley: University of California Press.
28.
Lieberson, S., and F. B. Lynn. 2002. Barking up the wrong branch: Alternatives to the current model of sociological science. Annual Review of Sociology28:1–19.
29.
Mackie, J.1974. The cement of the universe. Oxford: Oxford University Press. Corrected edition reissued in 2002.
30.
Newell, D. J.1992. Intention-to-treat analysis: Implications for quantitative and qualitative research. International Journal of Epidemiology21:837–841.
31.
Neyman, J.1923. Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes. Roczniki Nauk Rolniczych10:1–51. In Polish.
32.
Petitti, D. B.1998. Hormone replacement therapy and heart disease prevention: Experimentation trumps observation. Journal of the American Medical Association280:650–652.
33.
Petitti, D. B.2002. Hormone replacement therapy for prevention. Journal of the American Medical Association288:99–101.
34.
Pratt, J. W., and R. Schlaifer. 1984. On the nature and discovery of structure. Journal of the American Statistical Association79:9–33 (with discussion).
35.
Pratt, J. W., and R. Schlaifer. 1988. On the interpretation and observation of laws. Journal of Econometrics39:23–52.
36.
Robins, J. M.1999. Association, causation, and marginal structural models. Synthese121:151–179.
37.
Rubin, D.1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology66:688–701.
38.
Scharfstein, D. O., A. Rotnitzky, and J. M. Robins. 1999. Adjusting for non-ignorable dropout using semiparametric non-response models. Journal of the American Statistical Association94:1096-1146.
39.
Scheffé, H.1956. Models in the analysis of variance. Annals of Mathematical Statistics27:251–271.
40.
Smith, D. A., J. Kulik, and E. W. Stromsdorfer. 1984. The economic impact of the downriver community conference economic readjustment activity program: Choosing between retraining and job search placement strategies. In Displaced workers: Implications for educational and training institutions, ed. K Hollenbeck, F. Pratzner, and H. Rosen. Columbus, OH: National Center for Research in Vocational Education.
41.
Smith, G. D., and S. Ebrahim. 2005. Folate supplementation and cardiovascular disease. Lancet366:1679–1681.
42.
Sobel, M. E.2000. Causal inference in the social sciences. Journal of the American Statistical Association95:647–651.
43.
Sommer, A., and S. L. Zeger. 1991. On estimating efficacy from clinical trials. Statistics in Medicine10:45–52.
44.
Stone, R.1993. The assumptions on which causal inferences rest. Journal of the Royal Statistical Society Series B55:455–466.
45.
U.S. Preventive Services Task Force. 2003. Routine vitamin supplementation to prevent cancer and cardiovascular disease: Recommendations and rationale. Annals of Internal Medicine139:51–55.
46.
van der Vaart, A.1998. Asymptotic statistics. Cambridge: Cambridge University Press.
47.
Virtamo, J., P. Pietinen, J. K. Huttunen, P. Korhonen, N. Malila, M. J. Virtanen, D. Albanes, P. R. Taylor, P. Albert, and ATBC Study Group. 2003. Incidence of cancer and mortality following alpha-tocopherol and beta-carotene supplementation: A postintervention followup. Journal of the American Medical Association290:476–485.
