The Neyman-Rubin Model of Causal Inference and Estimation Via Matching Methods

The Oxford Handbook of Political Methodology

Volumn , Issue , 2008, Pages

  Sekhon, Jasjeet S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Causal inference; Causal mechanisms; Machine learning algorithms; Matching methods; Mill; Neyman Rubin model

Indexed keywords

EID: 84924033725     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/oxfordhb/9780199286546.003.0011     Document Type: Chapter

References (98)

1. ABADIE, A. 2002. Bootstrap tests for distributional treatment effect in instrumental variable models. Journal of the American Statistical Association, 97: 284-92.
2. ABADIE, A., and IMBENS, G. 2006. Large sample properties of matching estimators for average treatment effects. Econometrica, 74: 235-67.
3. BARNARD, J., FRANGAKIS, C. E., HILL, J. L., and RUBIN, D. B. 2003. Principal stratification approach to broken randomized experiments: a case study of school choice vouchers in New York City. Journal of the American Statistical Association, 98: 299-323.
4. BARTELS, L. 1996. Pooling disparate observations. American Journal of Political Science, 40: 905-42.
5. BENNETT, A. 1998. Causal inference in case studies: from Mill's methods to causal mechanisms. Presented at the annual meeting of the American Political Science Association, Atlanta.
6. BONNEY, J., and MINOZZI, B. C.-W. W. 2007. Issue accountability and the mass public: the electoral consequences of legislative voting on crime policy. Working paper.
7. BOWERS, J., and HANSEN, B. 2005. Attributing effects to a get-out-the-vote campaign using full matching and randomization inference. Working paper.
8. BRADY, H., and HUI, I. 2006. Is it worth going the extra mile to improve causal inference? Presented at the 23rd Annual Summer Meeting of the Society of Political Methodology.
9. CAMPBELL, D. T., and STANLEY, J. C. 1966. Experimental and Quasi-Experimental Designs for Research. Boston: Houghton Mifflin.
10. CHRISTAKIS, N. A., and IWASHYNA, T. I. 2003. The health impact of health care on families: a matched cohort study of hospice use by decedents and mortality outcomes in surviving, widowed spouses. Social Science and Medicine, 57: 465-75.
11. COCHRAN, W. G. 1953. Matching in analytical studies. American Journal of Public Health, 43: 684-91.
12. COCHRAN, W. G. 1965. The planning of observational studies of human populations (with discussion). Journal of the Royal Statistical Society, Series A, 128: 234-55.
13. COCHRAN, W. G., and RUBIN, D. B. 1973. Controlling bias in observational studies: a review. Sankhyā, Ser. A, 35: 417-46.
14. COHEN, M., and NAGEL, E. 1934. An Introduction to Logic and Scientific Method. New York: Harcourt, Brace.
15. COX, D. R. 1958. Planning of Experiments. New York: Wiley.
16. CRUMP, R. K., HOTZ, V. J., IMBENS, G. W., and MITNIK, O. A. 2006. Moving the goalposts: addressing limited overlap in estimation of average treatment effects by changing the estimand. Working paper.
17. DAWID, A. P. 1979. Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41: 1-31.
18. DAWID, A. P. 2000. Causal inference without counterfactuals (with discussion). Journal of the American Statistical Association, 95: 407-24.
19. DEHEJIA, R. 2005. Practical propensity score matching: a reply to Smith and Todd. Journal of Econometrics, 125: 355-64.
20. DEHEJIA, R., and WAHBA, S. 1997. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Ph.D. dissertation, Harvard University.
21. DEHEJIA, R., and WAHBA, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association, 94: 1053-62.
22. DEHEJIA, R., and WAHBA, S. 2002. Propensity score matching methods for nonexperimental causal studies. Review of Economics and Statistics, 84: 151-61.
23. DIAMOND, A., and SEKHON, J. S. 2005. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies.
24. DIPRETE, T. A., and ENGELHARDT, H. 2004. Estimating causal effects with matching methods in the presence and absence of bias cancellation. Sociological Methods and Research, 32: 501-28.
25. DORN, H. F. 1953. Philosophy of inference from retrospective studies. American Journal of Public Health, 43: 692-9.
26. ECKSTEIN, H. 1975. Case study and theory in political science. Pp. 79-137 in Handbook of Political Science, vii: Strategies of Inquiry, ed. F. I. Greenstein and N. W. Polsby. Reading, Mass.: Addison-Wesley.
27. FECHNER, G. T. 1966 [1860]. Elements of Psychophysics, vol i, trans. H. E. Adler, ed. D. H. Howes and E. G. Boring. New York: Rinehart and Winston.
28. FISHER, R. A. 1958a. Cancer and smoking. Nature, 182: 596.
29. FISHER, R. A. 1958b. Lung cancer and cigarettes? Nature, 182: 108.
30. FREEDMAN, D. A. 2008a. On regression adjustments in experiments with several treatments. Annals of Applied Statistics, 2: 176-96.
31. FREEDMAN, D. A. 2008b. On regression adjustments to experimental data. Advances in Applied Mathematics, 40: 180-93.
32. GALIANI, S., GERTLER, P., and SCHARGRODSKY, E. 2005. Water for life: the impact of the privatization of water services on child mortality. Journal of Political Economy, 113: 83-120.
33. GEORGE, A. L., and MC KEOWN, T. J. 1985. Case studies and theories of organizational decisionmaking. Pp. 21-58 in Advances in Information Processing in Organizations, ed. R. F. Coulam and R. A. Smith, Greenwich, Conn.: JAI Press.
34. GILLIGAN, M. J., and SERGENTI, E. J. 2006. Evaluating UN peacekeeping with matching to improve causal inference. Working paper.
35. GORDON, S., and HUBER, G. 2007. The effect of electoral competitiveness on incumbent behavior. Quarterly Journal of Political Science, 2: 107-38.
36. HANSEN, B. B. 2004. Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99: 609-18.
37. HECKMAN, J. J., ICHIMURA, H., SMITH, J., and TODD, P. 1998. Characterizing selection bias using experimental data. Econometrica, 66: 1017-98.
38. HERRON, M. C., and SEKHON, J. S. 2003. Overvoting and representation: an examination of overvoted presidential ballots in Broward and Miami-Dade Counties. Electoral Studies, 22: 21-47.
39. HERRON, M. C., and SEKHON, J. S. 2005. Black candidates and black voters: assessing the impact of candidate race on uncounted vote rates. Journal of Politics, 67: 154-77.
40. HERRON, M. C., and WAND, J., Forthcoming. Assessing partisan bias in voting technology: the case of the 2004 New Hampshire recount. Electoral Studies.
41. HILL, B. 1961. Principles of Medical Statistics, 7th edn. London: Lancet.
42. HOLLAND, P. W. 1986. Statistics and causal inference. Journal of the American Statistical Association, 81: 945-60.
43. HORVITZ, D. G., and THOMPSON, D. J. 1952. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47: 663-85.
44. IMAI, K. 2005. Do get-out-the-vote calls reduce turnout? The importance of statistical methods for field experiments. American Political Science Review, 99: 283-300.
45. IMBENS, G. W. 2000. The role of the propensity score in estimating dose-response functions. Biometrika, 87: 706-10.
46. LENZ, G. S., and LADD, J. M. 2006. Exploiting a rare shift in communication flows: media effects in the 1997 British election. Working paper.
47. LIEBERSON, S. 1991. Small N's and big conclusions: an examination of the reasoning in comparative studies based on a small number of cases. Social Forces, 70: 307-20.
48. LITTLE, D. 1998. Microfoundations, Method, and Causation. New Brunswick, NJ: Transaction.
49. MC KEOWN, T. J. 1999. Case studies and the statistical worldview: review of King, Keohane, and Verba 's Designing Social Inquiry: Scientific Inference in Qualitative Research. International Organization, 51: 161-90.
50. MAULDON, J., MALVIN, J., STILES, J., NICOSIA, N., and SETO, E. 2000. Impact of California's Cal-Learn demonstration project: final report. UC DATA Archive and Technical Assistance.
51. MEBANE, W. R., Jr., and SEKHON, J. S. 1998. Genetic optimization using derivatives (GENOUD). Software Package.
52. MEBANE, W. R., Jr., and SEKHON, J. S. 2004. Robust estimation and outlier detection for overdispersed multinomial models of count data. American Journal of Political Science, 48: 391-410.
53. MILL, J. S. 1872. A System of Logic, Ratiocinative and Inductive: Being a Connected View of the Principles of Evidence and the Methods of Scientific Investigation, 8th edn. London: Longmans, Green.
54. MITCHELL, A. F. S., and KRZANOWSKI, W. J. 1985. The Mahalanobis distance and elliptic distributions. Biometrika, 72: 464-7.
55. MITCHELL, A. F. S., and KRZANOWSKI, W. J. 1989. Amendments and corrections: the Mahalanobis distance and elliptic distributions. Biometrika, 76: 407.
56. MORGAN, S. L., and HARDING, D. J. 2006. Matching estimators of causal effects: prospects and pitfalls in theory and practice. Sociological Methods and Research, 35: 3-60.
57. NEYMAN, J. 1990 [1923]. On the application of probability theory to agricultural experiments: essay on principles, section 9, trans. D. M. Dabrowska and T. P. Speed. Statistical Science, 5: 465-72.
58. PARK, J. H. 2006. Causal effect of information on voting behavior from a natural experiment: an analysis of candidate blacklisting campaign in 2000 South Korean National Assembly election. Working paper.
59. PLEDGE, H. T. 1939. Science since 1500: A Short History of Mathematics, Physics, Chemistry [and] Biology. London: His Majesty's Stationery Office.
60. PRZEWORSKI, A., and TEUNE, H. 1970. The Logic of Comparative Social Inquiry. New York: Wiley.
61. RAESSLER, S., and RUBIN, D. B. 2005. Complications when using nonrandomized job training data to draw causal inferences. Proceedings of the International Statistical Institute.
62. RAGIN, C. C. 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press.
63. RAGIN, C. C., BERG-SCHLOSSER, D., and DE MEUR, G. 1996. Political methodology: qualitative methods. Pp. 749-68 in A New Handbook of Political Science, ed. R. E. Goodin and H.-D. Klingemann. New York: Oxford University Press.
64. ROBINSON, W. S. 1951. The logical structure of analytic induction. American Sociological Review, 16: 812-18.
65. ROSENBAUM, P. R. 1991. A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society, Series B, 53: 597-610.
66. ROSENBAUM, P. R. 2002. Observational Studies, 2nd edn. New York: Springer-Verlag.
67. ROSENBAUM, P. R. 2005. Heterogeneity and causality: unit heterogeneity and design sensitivity in observational studies. American Statistician, 59: 147-52.
68. ROSENBAUM, P. R., and RUBIN, D. B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70: 41-55.
69. ROSENBAUM, P. R., and RUBIN, D. B. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39: 33-8.
70. RUBIN, D. B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66: 688-701.
71. RUBIN, D. B. 1976a. Multivariate matching methods that are equal percent bias reducing, I: some examples. Biometrics, 32: 109-20.
72. RUBIN, D. B. 1976b. Multivariate matching methods that are equal percent bias reducing, II: maximums on bias reduction for fixed sample sizes. Biometrics, 32: 121-32.
73. RUBIN, D. B. 1977. Assignment to a treatment group on the basis of a covariate. Journal of Educational Statistics, 2: 1-26.
74. RUBIN, D. B. 1978. Bayesian inference for causal effects: the role of randomization. Annals of Statistics, 6: 34-58.
75. RUBIN, D. B. 1979. Using multivariate sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74: 318-28.
76. RUBIN, D. B. 1980. Bias reduction using Mahalanobis-metric matching. Biometrics, 36: 293-8.
77. RUBIN, D. B. 1997. Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127: 757-63.
78. RUBIN, D. B. 2001. Using propensity scores to help design observational studies: application to the tobaccolitigation. Health Services and Outcomes Research Methodology, 2: 169-88.
79. RUBIN, D. B. 2006. Matched Sampling for Causal Effects. Cambridge: Cambridge University Press.
80. RUBIN, D. B., and STUART, E. A. 2005. Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions. Working paper.
81. RUBIN, D. B., and THOMAS, N. 1992. Affinely invariant matching methods with ellipsoidal distributions. Annals of Statistics, 20: 1079-93.
82. SALMON, W. C. 1989. Four Decades of Scientific Explanation. Minneapolis: University of Minnesota Press.
83. SEAWRIGHT, J. 2004. Qualitative comparative analysis vis-a-vis regression. Presented at the meeting of the American Political Science Association.
84. SEKHON, J. S. 2004a. Quality meets quantity: case studies, conditional probability and counterfactuals. Perspectives on Politics, 2: 281-93.
85. SEKHON, J. S. 2004b. The varying role of voter information across democratic societies. Working paper.
86. SEKHON, J. S. 2006. The art of benchmarking: evaluating the performance of R on Linux and OS X. Political Methodologist, 14: 15-19.
87. SEKHON, J. S., Forthcoming. Multivariate, and propensity score matching software with automated balance optimization: the matching package for R. Journal of Statistical Software.
88. SEKHON, J. S., and MEBANE, W. R., Jr. 1998. Genetic optimization using derivatives: theory and application to nonlinear models. Political Analysis, 7: 189-203.
89. SKOCPOL, T. 1979. States and Social Revolutions: A Comparative Analysis of France, Russia, and China. Cambridge: Cambridge University Press.
90. SMITH, H. L. 1997. Matching with multiple controls to estimate treatment effects in observational studies. Sociological Methodology, 27: 305-53.
91. SMITH, J. A., and TODD, P. E. 2001. Reconciling conflicting evidence on the performance of propensity score matching methods. AEA Papers and Proceedings, 91: 112-18.
92. SMITH, J. A., and TODD, P. E. 2005a. Does matching overcome LaLonde's critique of nonexperimental estimators? Journal of Econometrics, 125: 305-53.
93. SMITH, J. A., and TODD, P. E. 2005b. Rejoinder. Journal of Econometrics, 125: 365-75.
94. STEERING COMMITTEE OF THE PHYSICIANS' HEALTH STUDY RESEARCH GROUP 1989. Final report on the aspirin component of the ongoing Physicians' Health Study. New England Journal of Medicine, 321: 129-35.
95. STIGLER, S. M. 1986. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, Mass.: Harvard University Press.
96. VANDENBROUCKE, J. P. 2001. In defense of case reports and case series. Annals of Internal Medicine, 134: 330-4.
97. WALDNER, D. 2002. Anti anti-determinism: or what happens when Schrödinger's cat and Lorenz 's butterfly meet Laplace's Demon in the study of political and economic development. Presented at the Annual Meeting of the American Political Science Association, Boston.
98. WINSHIP, C., and MORGAN, S. 1999. The estimation of causal effects from observational data. Annual Review of Sociology, 25: 659-707.