TRYGVE HAAVELMO and the EMERGENCE of CAUSAL CALCULUS

Econometric Theory

Volumn 31, Issue 1, 2015, Pages 152-179

  Pearl, Judea ^a  

^a Engineering IV   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84901895338     PISSN: 02664666     EISSN: 14694360     Source Type: Journal    
DOI: 10.1017/S0266466614000231     Document Type: Review

References (89)

1. Angrist, J., G. Imbens, & D. Rubin (1996) Identification of causal effects using instrumental variables (with comments). Journal of the American Statistical Association 91, 444-472.
2. Angrist, J.D. & J.-S. Pischke (2010) The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives 24, 3-30.
3. Balke, A. & J. Pearl (1995) Counterfactuals and policy analysis in structural models. In P. Besnard & S. Hanks (eds.), Uncertainty in Artificial Intelligence 11, pp. 11-18. Morgan Kaufmann.
4. Balke, A. & J. Pearl (1997) Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association 92, 1172-1176.
5. Bareinboim, E. & J. Pearl (2012) Controlling selection bias in causal inference. In N. Lawrence & M. Girolami (eds.), Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 100-108. JMLR.
6. Bareinboim, E., J. Tian, & J. Pearl (2014) Recovering from selection bias in causal and statistical inference. Technical report R-425, Department of Computer Science, University of California, Los Angeles, CA. Forthcoming Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI-14). Available at http://ftp.cs.ucla.edu/pub/statser/r425.pdf.
7. Brito, C. & J. Pearl (2002) Generalized instrumental variables. In A. Darwiche & N. Friedman (eds.), Uncertainty in Artificial Intelligence, Proceedings of the Eighteenth Conference, pp. 85-93. Morgan Kaufmann.
8. Brito, C. & J. Pearl (2006) Graphical condition for identification in recursive SEM. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, pp. 47-54. AUAI Press.
9. Campbell, D. & J. Stanley (1963) Experimental and Quasi-Experimental Designs for Research. Wadsworth Publishing.
10. Cartwright, N. (1989) Nature's Capacities and Their Measurement. Clarendon Press.
11. Cartwright, N. (2007) Hunting Causes and Using Them: Approaches in Philosophy and Economics. Cambridge University Press.
12. Chalak, K. & H. White (2011) An extended class of instrumental variables for the estimation of causal effects. Canadian Journal of Economics 44, 1-31.
13. Chen, B. & J. Pearl (2013) Regression and causation: A critical examination of econometrics textbooks. Real-World Economics Review 65, 2-20.
14. Dawid, A. (1979) Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B 41, 1-31.
15. Engle, R., D. Hendry, & J. Richard (1983) Exogeneity. Econometrica 51, 277-304.
16. Foygel, R., J. Draisma, & M. Drton (2012) Half-trek criterion for generic identifiability of linear structural equation models. The Annals of Statistics 40, 1682-1713.
17. Galles, D. & J. Pearl (1998) An axiomatic characterization of causal counterfactuals. Foundation of Science 3, 151-182.
18. Glymour,M. & S. Greenland (2008) Causal diagrams. In K. Rothman, S. Greenland, & T. Lash (eds.), Modern Epidemiology, 3rd ed., pp. 183-209. Lippincott Williams & Wilkins.
19. Goldberger, A. (1992) Models of substance; comment on N. Wermuth, 'On block-recursive linear regression equations'. Brazilian Journal of Probability and Statistics 6, 1-56.
20. Greenland, S. & J. Pearl (2011) Adjustments and their consequences-collapsibility analysis using graphical models. International Statistical Review 79, 401-426.
21. Haavelmo, T. (1943) The statistical implications of a system of simultaneous equations. Econometrica 11, 1-12.
22. Reprinted in D.F. Hendry & M.S. Morgan (eds.) (1995) The Foundations of Econometric Analysis, pp. 477-490. Cambridge University Press.
23. Haavelmo, T. (1944) The probability approach in econometrics (1944). Supplement to Econometrica 12, 12-17, 26-31, 33-39.
24. Halpern, J. (1998) Axiomatizing causal reasoning. In G. Cooper & S. Moral (eds.), Uncertainty in Artificial Intelligence, pp. 202-210. Morgan Kaufmann.
25. Halpern, J. (2000) Journal of Artificial Intelligence Research 12, 17-37.
26. Heckman, J. (1979) Sample selection bias as a specification error. Econometrica 47, 153-161.
27. Heckman, J. (2000) Causal parameters and policy analysis in economics: A twentieth century retrospective. The Quarterly Journal of Economics 115, 45-97.
28. Heckman, J. (2003) Conditioning causality and policy analysis. Journal of Econometrics 112, 73-78.
29. Heckman, J. (2008) Econometric causality. International Statistical Review 76, 1-27.
30. Heckman, J. (2010) Building bridges between structural and program evaluation approaches to evaluating policy. Journal of Economic Literature 48, 356-398.
31. Heckman, J. & R. Pinto (2013) Causal analysis after Haavelmo. Technical report NBER Technical Working paper 19453, National Bureau of Economic Research, MA.
32. Heckman, J. & E. Vytlacil (2007) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In Handbook of Econometrics, vol. 6B, pp. 4779-4874. Elsevier B.V.
33. Hirano, K. & G. Imbens (2001) Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology 2, 259-278.
34. Hoover, K.D. (2011) Counterfactuals and causal structure. In P.M. Illari, F. Russo, & J. Williamson (eds.), Causality in the Sciences, pp. 338-360. Clarendon Press.
35. Imbens, G. & J. Wooldridge (2009) Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 5-86.
36. Keane, M.P. (2010) A structural perspective on the experimentalist school. Journal of Economic Perspectives 24, 47-58.
37. Kyono, T. (2010) Commentator: A front-end user-interface module for graphical and structural equation modeling. Technical report R-364. Available at http://ftp.cs.ucla.edu/pub/statser/r364.pdf. Master thesis, Department of Computer Science, University of California, Los Angeles, CA.
38. Leamer, E.E. (2010) Tantalus on the road to asymptopia. Journal of Economic Perspectives 24, 31-46.
39. Lee, J.J. (2012) Correlation and causation in the study of personality. European Journal of Personality 26, 372-390.
40. Lewis, D. (1973) Counterfactuals. Harvard University Press.
41. Lucas Jr., R. (1976) Econometric policy evaluation: A critique. In K. Brunner & A. Meltzer (eds.), The Phillips Curve and Labor Markets, pp. 19-46. CRCS, vol. 1. North-Holland.
42. Marschak, J. (1953) Economic measurements for policy and prediction. InW.C. Hood & T. Koopmans (eds.), Studies in Econometric Method, pp. 1-26. Cowles Commission Monograph 10. Wiley and Sons, Inc.
43. Morgan, S. & C. Winship (2007) Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge University Press.
44. Nevo, A. & M.D. Whinston (2010) Taking the dogma out of econometrics: Structural modeling and credible inference. Journal of Economic Perspectives 24, 69-82.
45. Neyman, J. (1923) Sur les applications de la thar des probabilities aux experiences Agaricales: Essay des principle.
46. English translation of excerpts by D. Dabrowska&T. Speed (1990) Statistical Science 5, 463-472.
47. Oster (2013) Unobservable selection and coefficient stability: Theory and validation. Technical report, National Bureau of Economic Research.
48. Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
49. Pearl, J. (1993) Comment: Graphical models, causality, and intervention. Statistical Science 8, 266-269.
50. Pearl, J. (1994) A probabilistic calculus of actions. In R.L. de Mantaras & D. Poole (eds.), Uncertainty in Artificial Intelligence 10, pp. 454-462. Morgan Kaufmann.
51. Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press. 2nd ed., 2009.
52. Pearl, J. (2001) Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 411-420. Morgan Kaufmann.
53. Pearl, J. (2004) Robustness of causal claims. In M. Chickering & J. Halpern (eds.), Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, pp. 446-453. AUAI Press.
54. Pearl, J. (2009a) Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press.
55. Pearl, J. (2009b) Myth, confusion, and science in causal analysis. Technical report R-348, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/statser/r348.pdf.
56. Pearl, J. (2010a) The foundations of causal inference. Sociological Methodology 40, 75-149.
57. Pearl, J. (2010b) An introduction to causal inference. The International Journal of Biostatistics 6, Iss. 2, Article 7; doi:10.2202/1557-4679.1203. Available at http://ftp.cs.ucla.edu/pub/statser/r354-corrected-reprint.pdf.
58. Pearl, J. (2010c) On the consistency rule in causal inference: Axiom, definition, assumption, or theorem? Epidemiology 21, 872-875.
59. Pearl, J. (2010d) Review of N. Cartwright 'Hunting causes and using them'. Economics and Philosophy 26, 69-77.
60. Pearl, J. (2011a) Graphical models, potential outcomes and causal inference: Comment on Lindquist and Sobel. Statistics in Medicine 58, 770-771.
61. Pearl, J. (2011b) Principal stratification a goal or a tool? The International Journal of Biostatistics 7, Iss. 1, Article 20; doi:10.2202/1557-4679.1322. Available at http://ftp.cs.ucla.edu/pub/statser/r382.pdf.
62. Pearl, J. (2011c) Understanding bias amplification. American Journal of Epidemiology 174, 1223-1227; doi:10.1093/aje/kwr352.
63. Pearl, J. (2012a) The causal foundations of structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling, pp. 68-91. Guilford Press.
64. Pearl, J. (2012b) The causal mediation formula - a guide to the assessment of pathways and mechanisms. Prevention Science 13, 426-436; doi:10.1007/s11121-011-0270-1.
65. Pearl, J. (2014) Interpretation and identification of causal mediation. Technical report R-389, Department of Computer Science, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/statser/r389.pdf. Forthcoming Psychological Methods, 2014.
66. Pearl, J. (2013a) Lindear models: A useful "microscope" for causal analysis. Journal of Causal Inference 1, 155-170.
67. Pearl, J. (2013b) Reflections on Heckman and Pinto's 'causal analysis after Haavelmo'. Technical report R-420, Department of Computer Science, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/statser/r420.pdf. Working paper.
68. Pearl, J. & E. Bareinboim (2011) Transportability of causal and statistical relations: A formal approach. In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11). Available at http://ftp.cs.ucla.edu/pub/statser/r372a.pdf.
69. Pearl, J. & A. Paz (1986) On the logic of representing dependencies by graphs. In Proceedings of the Canadian AI Conference, Montreal, Ontario, Canada, pp. 94-98.
70. Pearl, J. & T. Verma (1987) The logic of representing dependencies by directed graphs. In Proceedings of the Sixth National Conference on Artificial Intelligence, pp. 374-379. Morgan Kaufmann Publishers.
71. Pearl, J. & T. Verma (1991) A theory of inferred causation. In J. Allen, R. Fikes, & E. Sandewall (eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pp. 441-452. Morgan Kaufmann.
72. Phiromswad, P. & K.D. Hoover (2013) Selecting instrumental variables: A graph-theoretic approach. Available at SSRN: http://ssrn.com/abstract=2318552 or http://dx.doi.org/10.2139/ssrn.2318552.
73. Richard, J. (1980) Models with several regimes and changes in exogeneity. Review of Economic Studies 47, 1-20.
74. Rosenbaum, P. & D. Rubin (1983) The central role of propensity score in observational studies for causal effects. Biometrika 70, 41-55.
75. Rubin, D. (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
76. Rubin, D. (2009) Author's reply: Should observational studies be designed to allow lack of balance in covariate distributions across treatment group? Statistics in Medicine 28, 1420-1423.
77. Rubin, D. (2010) Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). Psychological Methods 15, 39-46.
78. Shpitser, I. & J. Pearl (2008) Complete identification methods for the causal hierarchy. Journal of Machine Learning Research 9, 1941-1979.
79. Simon, H. & N. Rescher (1966) Cause and counterfactual. Philosophy and Science 33, 323-340.
80. Sims, C.A. (2010) But economics is not an experimental science. Journal of Economic Perspectives 24, 59-68.
81. Spanos, A. (2010) Theory testing in economics and the error-statistical perspective. In D.G. Mayo & A. Spanos (eds.), Error and Inference, pp. 202-246. Cambridge University Press.
82. Spirtes, P., C. Glymour, & R. Scheines (1993) Causation, Prediction, and Search. Springer-Verlag.
83. Stock, J. & M. Watson (2011) Introduction to Econometrics, 3rd ed. Addison-Wesley.
84. Strotz, R. & H. Wold (1960) Recursive versus nonrecursive systems: An attempt at synthesis. Econometrica 28, 417-427.
85. Vansteelandt, S. & C. Lange (2012) Causation and causal inference for genetic effects. Human Genetics 131, 1665-1676. Special Issue on Genetic Epidemiology: Study Designs and Methods Post-GWAS.
86. Verma, T. & J. Pearl (1990) Equivalence and synthesis of causal models. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA.
87. Also in P. Bonissone, M. Henrion, L.N. Kanal, & J.F. Lemmer (eds.) (1991) Uncertainty in Artificial Intelligence 6, pp. 255-268. Elsevier Science Publishers, B.V.
88. Wermuth, N. (1992) On block-recursive regression equations (with discussion). Brazilian Journal of Probability and Statistics 6, 1-56.
89. White, H. & X. Lu (2010) Robustness checks and robustness tests in applied economics. Technical report, Department of Economics, University of California, San Diego, CA. Discussion paper.