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CAUSAL ANALYSIS AFTER HAAVELMO

Published online by Cambridge University Press:  10 June 2014

James Heckman  and
Rodrigo Pinto
James Heckman
Affiliation:
The University of Chicago
Rodrigo Pinto*
Affiliation:
The University of Chicago
*
*Address correspondence to Rodrigo Pinto, Department of Economics, The University of Chicago, 1155 E. 60th St., Room 215, Chicago, IL 60637, USA; e-mail: [email protected].
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Abstract

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890) ceteris paribus analysis. We embed Haavelmo’s framework into the recursive framework of Directed Acyclic Graphs (DAGs) commonly used in the literature of causality (Pearl, 2000) and Bayesian nets (Lauritzen, 1996). We compare the analysis of causality based on a methodology inspired by Haavelmo’s ideas with other approaches used in the causal literature of DAGs. We discuss the limitations of methods that solely use the information expressed in DAGs for the identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo.

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 1: Haavelmo Memorial Issue: Part One , February 2015 , pp. 115 - 151
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Angrist, J.D. & Pischke, J.S. (2008) Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.CrossRefGoogle Scholar
Berkeley, G. (1710) A Treatise Concerning the Principles of Human Knowledge. JB Lippincott & Company.Google Scholar
Bishop, Y.M., Fienberg, S.E., & Holland, P.W. (1975) Discrete Multivariate Analysis: Theory and Practice. The MIT Press.Google Scholar
Blundell, R. & Powell, J. (2003) Endogeneity in nonparametric and semiparametric regression models. In Dewatripont, L.P.H.M. & Turnovsky, S.J. (eds.), Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress, vol. 2. Cambridge UniversityPress.Google Scholar
Chalak, K. & White, H. (2012) Causality, conditional independence, and graphical separation in settable systems. Neural Computation 24(7), 1611–1668.CrossRefGoogle Scholar
Chesher, A. & Rosen, A. (2012) Simultaneous Equations for Discrete Outcomes: Coherence, Completeness, and Identification. Working papers CWP21/12, cemmap.Google Scholar
Dawid, A. (2001) Separoids: A mathematical framework for conditional independence and irrelevance. Annals of Mathematics and Artificial Intelligence 32(1–4), 335–372.CrossRefGoogle Scholar
Dawid, A.P. (1979) Conditional independence in statistical theory (with discussion). Journal of the Royal Statistical Society, Series B (Statistical Methodology) 41(1), 1–31.Google Scholar
Fechner, G.T. (1851) Outline of a new principle of mathematical psychology. Psychological Research 49, 203–207.CrossRefGoogle Scholar
Freedman, D. & Humphreys, P. (2010) “The Grand Leap” In Collier, D., Sekhon, J., & Stark, P. (eds.), Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Ch.14, pp. 243–254Cambridge University Press.Google Scholar
Frisch, R. (1930, published 2010) “General considerations in Statics and Dynamics in Economics,” In Bjerkholt, O. & Qin, D. (eds.), A Dynamic Approach to Economic Theory: The Yale Lectures of Ragnar Frisch, 1930. ch. 1, pp. 29–81Routledge.Google Scholar
Frisch, R. (1938) Statistical versus theoretical relations in economic macrodynamics. Paper given at League of Nations. Reprinted in Hendry, D.F. & Morgan, M.S. (1995) The Foundations of Econometric Analysis. Cambridge University Press.Google Scholar
Galton, F. (1896) Notes to the memoir by Professor Karl Pearson, F.R.S., on spurious correlation. Proceedings of the Royal Society of London 60, 498–502.Google Scholar
Goth, G. (2006) Judea Pearl interview. IEEE Internet Computing 10(5), 6.CrossRefGoogle Scholar
Haavelmo, T. (1943, January) The statistical implications of a system of simultaneous equations. Econometrica 11(1), 1–12.CrossRefGoogle Scholar
Haavelmo, T. (1944) The probability approach in econometrics. Econometrica 12(Supplement), iii–vi and 1–115.Google Scholar
Hansen, L.P. & Sargent, T.J. (1980, February) Formulating and estimating dynamic linear rational expectations models. Journal of Economic Dynamics and Control 2(1), 7–46.CrossRefGoogle Scholar
Heckman, J.J. (1976, December) The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals ofEconomic and Social Measurement 5(4), 475–492.Google Scholar
Heckman, J.J. (1978, July) Dummy endogenous variables in a simultaneous equation system. Econometrica 46(4), 931–959.CrossRefGoogle Scholar
Heckman, J.J. (1979, January) Sample selection bias as a specification error. Econometrica 47(1), 153–162.CrossRefGoogle Scholar
Heckman, J.J. (2005, August) The scientific model of causality. Sociological Methodology 35(1), 1–97.CrossRefGoogle Scholar
Heckman, J.J. (2008a, April) Econometric causality. International Statistical Review 76(1), 1–27.CrossRefGoogle Scholar
Heckman, J.J. (2008b) The principles underlying evaluation estimators with an application to matching. Annales d’Economie et de Statistiques 91–92, 9–73.CrossRefGoogle Scholar
Heckman, J.J. & MaCurdy, T.E. (1985, February) A simultaneous equations linear probability model. Canadian Journal of Economics 18(1), 28–37.CrossRefGoogle Scholar
Heckman, J. & Pinto, R. (2014) A Unified Approach to Examine Treatment Effects: Causality and Identification. Unpublished manuscript, University of Chicago, Department of Economics.Google Scholar
Heckman, J.J. & Robb, R. (1985, October–November) Alternative methods for evaluating the impact of interventions: An overview. Journal of Econometrics 30(1–2), 239–267.CrossRefGoogle Scholar
Heckman, J.J. & Vytlacil, E.J. (1999, April) Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proceedings of the National Academy of Sciences 96(8), 4730–4734.CrossRefGoogle ScholarPubMed
Heckman, J.J. & Vytlacil, E.J. (2005, May) Structural equations, treatment effects and econometric policy evaluation. Econometrica 73(3), 669–738.CrossRefGoogle Scholar
Heckman, J.J. & Vytlacil, E.J. (2007a) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In Heckman, J. & Leamer, E. (eds.), Handbook of Econometrics, vol. 6B, pp. 4779–4874. Elsevier.CrossRefGoogle Scholar
Heckman, J.J. & Vytlacil, E.J. (2007b) Econometric evaluation of social programs, part II: Using the marginal treatment effect to organize alternative economic estimators to evaluate social programs and to forecast their effects in new environments. In Heckman, J. & Leamer, E. (eds.), Handbook of Econometrics, vol. 6B, Ch. 71, pp. 4875–5143. Elsevier.CrossRefGoogle Scholar
Heidelberger, M. (2004) Nature from Within: Gustav Theodor Fechner and His Psychophysical Worldview. University of Pittsburgh Press.CrossRefGoogle Scholar
Holland, P.W. (1986, December) Statistics and causal inference. Journal of the American Statistical Association 81(396), 945–960.CrossRefGoogle Scholar
Howard, R.A. & Matheson, J.E. (1981) Principles and applications of decision analysis. In Influence Diagrams, 1st ed., pp. 720–762. Stanford Research Institute.Google Scholar
Huang, Y. & Valtorta, M. (2006) A Study of Identifiability in Causal Bayesian Network. Technical report, University of South Carolina Department of Computer Science.Google Scholar
Imbens, G.W. & Angrist, J.D. (1994, March) Identification and estimation of local average treatment effects. Econometrica 62(2), 467–475.CrossRefGoogle Scholar
Kiiveri, H., Speed, T.P., & Carlin, J.B. (1984) Recursive causal models. Journal of the Australian Mathematical Society (Series A) 36(1), 30–52.CrossRefGoogle Scholar
Koopmans, T.C. & Reiersøl, O. (1950, June) The identification of structural characteristics. The Annals of Mathematical Statistics XXI(2), 165–181.CrossRefGoogle Scholar
Koopmans, T.C., Rubin, H., & Leipnik, R.B. (1950) Measuring the equation systems of dynamic economics. In Koopmans, T.C. (ed.), Statistical Inference in Dynamic Economic Models. Number 10 in Cowles Commission Monograph, Ch. 2, pp. 53–237. John Wiley & Sons.Google Scholar
Lauritzen, S.L. (1996) Graphical Models. Clarendon Press.CrossRefGoogle Scholar
Lauritzen, S.L. (2001) Causal inference from graphical models. In Barndorff-Nielsen, O., Cox, D.R., & Klüppelberg, C. (eds.), Complex Stochastic Systems, pp. 63–107. Chapman and Hall.Google Scholar
Lauritzen, S.L. & Richardson, T.S. (2002) Chain graph models and their causal interpretations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(3), 321–348.CrossRefGoogle Scholar
Lehmann, E.L. & Romano, J.P. (2005) Testing Statistical Hypotheses, 3rd ed. Springer Science and Business Media.Google Scholar
Margolis, M., List, J., & Osgood, D. (2012, April) Endangered Options and Endangered Species: What We Can Learn from a Dubious Design. Unpublished manuscript, Gettysburg College, Department of Economics.Google Scholar
Marshall, A. (1890) Principles of Economics. Macmillan and Company.Google Scholar
Mas-Colell, A., Whinston, M.D., & Green, J.R. (1995) Microeconomic Theory. Oxford University Press.Google Scholar
Matzkin, R.L. (2007) Nonparametric identification. In Heckman, J. & Leamer, E. (eds.), Handbook of Econometrics, vol. 6B. Elsevier.Google Scholar
Matzkin, R.L. (2008) Identification in nonparametric simultaneous equations models. Econometrica 76(5), 945–978.Google Scholar
Matzkin, R.L. (2012) Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity. Journal of Econometrics 166(1), 106–115.CrossRefGoogle Scholar
Matzkin, R.L. (2013) Nonparametric identification of structural economic models. Annual Review of Economics 5, 457–486.CrossRefGoogle Scholar
Newcomb, S. (1886) Principles of Political Economy. Harper & Brothers.Google Scholar
Newey, W.K. & Powell, J.L. (2003, September) Instrumental variable estimation of nonparametric models. Econometrica 71(5), 1565–1578.CrossRefGoogle Scholar
Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc.Google Scholar
Pearl, J. (1993) [Bayesian Analysis in Expert Systems]: Comment: Graphical models, causality and intervention. Statistical Science 8(3), 266–269.CrossRefGoogle Scholar
Pearl, J. (1995, December) Causal diagrams for empirical research. Biometrika 82(4), 669–688.CrossRefGoogle Scholar
Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press.Google Scholar
Pearl, J. (2001) Causality: Models, Reasoning, and Inference (Reprinted with corrections ed.).Cambridge University Press.Google Scholar
Pearl, J. (2009) Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press.CrossRefGoogle Scholar
Pearl, J. & Verma, T.S. (1994) A theory of inferred causation. In Prawitz, D., Skyrms, B., & Westerståhl, D. (eds.), Logic, Methodology, and Philosophy of Science, vol. IX, pp. 789–812.Elsevier Science. Proceedings of the Ninth International Congress of Logic, Methodology, and Philosophy of Science, Uppsala, Sweden, August 7–14, 1991.Google Scholar
Powell, J.L. (1994) Estimation of semiparametric models. In Engle, R. & McFadden, D. (eds.), Handbook of Econometrics, vol. 4, pp. 2443–2521. Elsevier.Google Scholar
Reiersöl, O. (1945) Confluence analysis by means of instrumental sets of variables. Arkiv för Matematik, Astronomi och Fysik 32A(4), 1–119.Google Scholar
Robins, J. (1986) A new approach to causal inference in mortality studies with a sustained exposure period: Application to control of the healthy worker survivor effect. Mathematical Modelling 7(9–12), 1393–1512.CrossRefGoogle Scholar
Rosenbaum, P.R. & Rubin, D.B. (1983, April) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55.CrossRefGoogle Scholar
Rubin, D.B. (1986) Statistics and causal inference: Comment: Which ifs have causal answers. Journal of the American Statistical Association 81(396), 961–962.Google Scholar
Simon, H.A. (1953) Causal ordering and identifiability. In Hood, W.C. & Koopmans, T.C. (eds.), Studies in Econometric Method, Ch. 3, pp. 49–74. John Wiley & Sons, Inc.Google Scholar
Sobel, M.E. (2005) Discussion: ‘The Scientific Model of Causality’. Sociological Methodology 35(1), 99–133.Google Scholar
Spirtes, P. (1995) Directed cyclic graphical representations of feedback models. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95), pp. 491–498. Morgan Kaufmann.Google Scholar
Spirtes, P., Glymour, C.N., & Scheines, R. (2000) Causation, Prediction and Search, 2nd ed. MIT Press.Google Scholar
Tamer, E. (2003, January) Incomplete simultaneous discrete response model with multiple equilibria. Review of Economic Studies 70(1), 147–165.CrossRefGoogle Scholar
Vytlacil, E.J. (2002, January) Independence, monotonicity, and latent index models: An equivalence result. Econometrica 70(1), 331–341.CrossRefGoogle Scholar
White, H. & Chalak, K. (2009) Settable systems: An extension of Pearl’s causal model with optimization, equilibrium, and learning. Journal of Machine Learning Research 10, 1759–1799.Google Scholar
Yule, G.U. (1895) On the correlation of total pauperism with proportion of out-relief. The Economic Journal 5(20), 603–611.CrossRefGoogle Scholar