Separated at Birth: Statisticians, Social Scientists, and Causality in Health Services Research
Bryan E. Dowd,
Bryan E. Dowd
Division of Health Policy and Management, School of Public Health, University of Minnesota, PO Box 729 MMC, Minneapolis, MN 55455
Address correspondence to Bryan E. Dowd, Ph.D., Division of Health Policy and Management, School of Public Health, University of Minnesota, PO Box 729 MMC, Minneapolis, MN 55455; e-mail: [email protected].
Search for more papers by this authorBryan E. Dowd,
Bryan E. Dowd
Division of Health Policy and Management, School of Public Health, University of Minnesota, PO Box 729 MMC, Minneapolis, MN 55455
Address correspondence to Bryan E. Dowd, Ph.D., Division of Health Policy and Management, School of Public Health, University of Minnesota, PO Box 729 MMC, Minneapolis, MN 55455; e-mail: [email protected].
Search for more papers by this authorAbstract
Objective. Health services research is a field of study that brings together experts from a wide variety of academic disciplines. It also is a field that places a high priority on empirical analysis. Many of the questions posed by health services researchers involve the effects of treatments, patient and provider characteristics, and policy interventions on outcomes of interest. These are causal questions. Yet many health services researchers have been trained in disciplines that are reluctant to use the language of causality, and the approaches to causal questions are discipline specific, often with little overlap. How did this situation arise? This paper traces the roots of the division and some recent attempts to remedy the situation.
Data Sources and Settings. Existing literature.
Study Design. Review of the literature.
Supporting Information
Appendix SA1: Two Derivations of the IV Estimator.
Appendix SA2: RCTs and Heterogeneous Treatment Effects.
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REFERENCES
- Angrist, J. D., G. W. Imbens, and D. B. Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association 91 (434): 444–55.
- Berwick, D. 2008. “The Science of Improvement. Journal of the American Medical Association 299 (10): 1182–4.
- Cartwright, N. 2007. “ Counterfactuals in Economics.” In Causation and Explanation, edited by J. K. Campbell, M. O'Rourke, H. S. Silverstein, and H. Silverstein, pp. 191–216. Cambridge, MA: MIT Press.
- Cayley, A. 1854. “‘On the Theory of Groups as Depending on the Symbolic Equation θn=1’, 7:4 Philosophical Magazine 7 (42): 40–47. Also ‘A Memoir on the Theory of Matrices’. Philosophical Transactions of the Royal Society of London (1858): 17–37.
- Cook, T. D. 2008. “‘Waiting for Life to Arrive’: A History of the Regression-Discontinuity Design in Psychology, Statistics and Economics. Journal of Econometrics 142 (2): 636–54.
- Dawid, A. P. 2000. “Causal Analysis without Counterfactuals. Journal of the American Statistician 95 (450): 407–24.
- Deaton, A. S. 2009. Instruments of Development: Randomization in the Tropics and the Search for the Elusive Keys to Economic Development. National Bureau of Economic Research, Working Paper 14690 [accessed on October 22, 2010]. Available at http://www.nber.org/papers/w14690
- Dowd, B., and R. Town. 2002. “Does X Really Cause Y? Academy Health: Changes in Health Care Financing and Organization” [accessed on October 22, 2010]. Available at http://www.hcfo.net/pdf/xy.pdf
- Duncan, O. D. 1966. “Path Analysis: Sociological Examples. American Journal of Sociology 72: 1–16.
- Earle, C. C., J. S. Tsai, R. D. Gelber, M. C. Weinstein, P. J. Neumann, and J. C. Weeks. 2001. “Effectiveness of Chemotherapy for Advanced Lung Cancer in the Elderly: Instrumental Variable and Propensity Analysis. Journal of Clinical Oncology 19 (4): 1064–70.
- Fisher, R. 1926. “The Arrangement of Field Experiments. Journal of Ministry of Agriculture 33: 500–13.
- Freedman, D. 1999. “Some Remarks on the History of Statistics. Statistical Science 14 (3): 243–58.
- Greenland, S. 2000. “An Introduction to Instrumental Variables for Epidemiologists. International Journal of Epidemiology 29: 722–9.
- Harris, K. M., and D. K. Remler. 1998. “Who is the Marginal Patient? Understanding Instrumental Variables Estimates of Treatment Effects. Health Services Research 33 (5, part 1): 1337–60.
- Heckman, J. 1974. “Shadow Prices, Market Wages, and Labor Supply. Econometrica 42 (4): 679–94.
- Heckman, J. 1996. “Identification of Causal Effects Using Instrumental Variables: Comment. Journal of the American Statistical Association 91 (434): 459–62.
- Heckman, J. 1999. “ Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective.” Working Paper 7333. Cambridge, MA: National Bureau of Economic Research [accessed on October 22, 2010]. Available at http://www.nber.org/papers/w7333
- Heckman, J. 2006. “The Scientific Model of Causality. Sociological Methodology 35 (1): 1–98.
- Heckman, J. 2008. “Econometric Causality. International Statistical Review 76 (1): 1–27.
- Heckman, J. 2010. “Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy. Journal of Economic Literature 48 (2): 356–98.
-
Heckman, J.,
R. Lalonde, and J. Smith. 1999. “ The Economics and Econometrics of Active Labor Market Programs.”
In
Handbook of Labor Economics: Vol. 3, Chapter 31, edited by
O. Ashenfelter and
D. Card, pp. 1865–2097. Amsterdam: North Holland.
10.1016/S1573-4463(99)03012-6 Google Scholar
- Hernan, M. A., and J. M. Robins. 2006. “Instruments for Causal Inference: An Epidemiologist's Dream? Epidemiology 17 (4): 360–72.
- Holland, P. 1986. “Statistics and Causal Inference. Journal of the American Statistical Association 81 (396): 945–60.
- Johnstone, D. J. 1989. “On the Necessity of Random Sampling. British Journal for the Philosophy of Science 40 (4): 443–57.
- Kempthorne, O. 1978. “A Biometrics Invited Paper: Logical, Epistemological and Statistical Aspects of Nature-Nurture Data Interpretation. Biometrics 34 (1): 1–23.
- Knight, F. 1921. Risk, Uncertainty and Profit. New York: Houghton Mifflin Company.
- Kuhn, T. S. 1962. The Structure of Scientific Revolutions. Chicago, IL: University of Chicago Press.
- Lee, L-F. 1976. “Estimation of Limited Dependent Variables by Two Stage Method.” Unpublished Ph.D. Thesis, Department of Economics, University of Rochester.
- Lee, L-F. 1983. “Generalized Econometric Models with Selectivity. Econometrica 51 (2): 507–12.
- Lucas, R E. 1976. “ Econometric Policy Evaluation: A Critique.” In The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy, Vol. 1, edited by K. Brunner and A. H. Meltzer, pp. 19–46. Amsterdam: North-Holland.
- Manning, W. G., N. Duan, and W. H. Rogers. 1987. “Monte Carlo Evidence on the Choice between Sample Selection and Two-Part Models. Journal of Econometrics 35: 59–82.
- Martens, E. P., W. R. Pestman, A. de Boer, S. V. Belitser, and O. H. Klungel. 2006. “Instrumental Variables: Application and Limitations. Epidemiology 17 (3): 260–7.
- McClellan, M., B. McNeil, and J. Newhouse. 1994. “Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality? Journal of the American Medical Association 272 (11): 859–66.
- Moffitt, R. A. 1996. “Comment. Journal of the American Statistical Association 91 (434): 462–5.
- Monfardini, C., and R. Radice. 2008. “Testing Exogeneity in the Bivariate Probit Model: A Monte Carlo study. Oxford Bulletin of Economics and Statistics 70 (2): 271–82.
- Murray, M. 2006. “Avoiding Invalid Instruments and Coping with Weak Instruments. Journal of Economic Perspectives 20 (4): 111–32.
- Olsen, R. 1980. “A Least-Squares Correction for Selectivity Bias. Econometrica 48 (7): 1815–20.
- Pearl, J. 1997. “The New Challenge: From a Century of Statistics to an Age of Causation.” Computer Science Department, University of California, Los Angeles, CA [accessed on October 22, 2010]. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.2380&rep=rep1&type=
- Pearl, J. 2000. Causality: Models, Reasoning and Inference. Cambridge, U.K.: Cambridge University Press.
-
Pearson, K.
1920. “Notes on the History of Correlation.
Biometrika
13
(1): 25–45.
10.1093/biomet/13.1.25 Google Scholar
-
Penrose, R.
1989, 2002. The Emperor's New Mind. New York: Oxford University Press
(citations are from the 1989 edition).
10.1093/oso/9780198519737.001.0001 Google Scholar
- Penrose, R. 2005, 2007. The Road to Reality. New York: Knopf Publishing Group (citations are from the 2005 edition).
- Polanyi, M. 1962. “The Unaccountable Element in Science. Philosophy: The Journal of the Royal Institute of Philosophy 37: 1–14.
- Polanyi, M. 1968. “Logic and Psychology. American Psychologist 23: 27–43.
- Rassen, J. A., M. A. Brookhart, R. J. Glynn, M. A. Mittleman, and S. Schneeweiss. 2009a. “Instrumental Variables I: Instrumental Variables Exploit Natural Variation in Non-Experimental Data to Estimate Causal Relationships. Journal of Clinical Epidemiology 62 (12): 1226–1232.
- Rassen, J. A. 2009b. “Instrumental Variables II: Instrumental Variable Application—In 25 Variations, the Physician Prescribing Preference Generally Was Strong and Reduced Covariate Imbalance. Journal of Clinical Epidemiology 62 (12): 1233–1241.
- Rosenbaum, P. R., and D. B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70 (1): 41–55.
- Rosenbaum, P. R., and D. B. Rubin. 1984. “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score. Journal of the American Statistical Association 79 (387): 516–24.
- Rosenberg, A. 1989. “Russell versus Steiner on Physics and Causality. Philosophy of Science 56 (2): 341–7.
- Rubin, D. B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology 66: 688–701.
-
Russell, B.
1913. “On the Notion of Cause.
Proceedings of the Aristotelian Society
13: 1–26.
10.1093/aristotelian/13.1.1 Google Scholar
- Saris, W., and H. Stronkhorst. 1984. Causal Modeling in Non-Experimental Research. Amsterdam: Sociometric Research Foundation.
- Shadish, W. R., T. D. Cook, and D. T. Campbell. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin Company.
- Steiner, M. 1986. “Events and Causality. Journal of Philosophy 83 (5): 249–64.
- Stock, J. H., and F. Trebbi. 2003. “Retrospectives: Who Invented Instrumental Variable Regression? Journal of Economic Perspectives 17 (3): 177–94.
- Stukel, T. A., E. S. Fisher, D. E. Wennberg, D. A. Alter, D. J. Gottlieb, and M. J. Vermeulen. 2007. “Analysis of Observational Studies in the Presence of Treatment Selection Bias: Effects of Invasive Cardiac Management on AMI Survival Using Propensity Score and Instrumental Variable Methods. Journal of the American Medical Association 297 (3): 278–85.
- Suppes, P. C. 1970. A Probabilistic Theory of Causality. Amsterdam: North Holland.
- Urbach, P. 1985. “Randomization and the Design of Experiments. Philosophy of Science 52: 256–73.
- Wright, P. 1928. “The Tariff on Animal and Vegetable Oils: Appendix B.” Reproduced in Stock and Trebbi (2003).
- Wright, S. 1918. “On the Nature of Size Factors. Genetics 3: 367–74.
- Worrall, J. 2002. “What Evidence in Evidence-Based Medicine? Philosophy of Science 69: S216–330.
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