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Causal Analysis after Haavelmo

Author

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  • Heckman, James J.

    () (University of Chicago)

  • Pinto, Rodrigo

    () (University of California, Los Angeles)

Abstract

Haavelmo's seminal 1943 paper is the first rigorous treatment of causality. In it, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are de fined 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 (DAG) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare an approach based on Haavelmo's methodology with a standard approach in the causal literature of DAGs – the "do-calculus" of Pearl (2009). We discuss the limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo (1944). In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover it.

Suggested Citation

  • Heckman, James J. & Pinto, Rodrigo, 2013. "Causal Analysis after Haavelmo," IZA Discussion Papers 7628, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp7628
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
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    4. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    5. Karim Chalak & Halbert White, 2008. "Causality, Conditional Independence, and Graphical Separation in Settable Systems," Boston College Working Papers in Economics 689, Boston College Department of Economics, revised 04 Jul 2010.
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    7. James J. Heckman & Thomas E. MaCurdy, 1985. "A Simultaneous Equations Linear Probability Model," Canadian Journal of Economics, Canadian Economics Association, vol. 18(1), pages 28-37, February.
    8. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    9. Steffen L. Lauritzen & Thomas S. Richardson, 2002. "Chain graph models and their causal interpretations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 321-348.
    10. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
    11. Wagner, Alfred, 1891. "Marshall's Principles of Economics," History of Economic Thought Articles, McMaster University Archive for the History of Economic Thought, vol. 5, pages 319-338.
    12. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
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    14. Elie Tamer, 2003. "Incomplete Simultaneous Discrete Response Model with Multiple Equilibria," Review of Economic Studies, Oxford University Press, vol. 70(1), pages 147-165.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Predictive Modeling, Causal Inference, and Imbens-Rubin (Among Others)
      by Francis Diebold in No Hesitations on 2014-05-06 18:10:00

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    Cited by:

    1. Tapia, Jose, 2015. "Profits encourage investment, investment dampens profits, government spending does not prime the pump — A DAG investigation of business-cycle dynamics," MPRA Paper 64985, University Library of Munich, Germany, revised Jun 2015.
    2. Jorge Luis García & James J. Heckman & Duncan Ermini Leaf & María José Prados, 2017. "Quantifying the Life-cycle Benefits of a Prototypical Early Childhood Program," NBER Working Papers 23479, National Bureau of Economic Research, Inc.
    3. Ran Spiegler, 2016. "Bayesian Networks and Boundedly Rational Expectations," The Quarterly Journal of Economics, Oxford University Press, vol. 131(3), pages 1243-1290.
    4. James J. Heckman & John Eric Humphries & Gregory Veramendi, 2018. "The Nonmarket Benefits of Education and Ability," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 282-304.
    5. Jorge Luis García & James J. Heckman & Duncan Ermini Leaf & María José Prados, 2016. "The Life-cycle Benefits of an Influential Early Childhood Program," NBER Working Papers 22993, National Bureau of Economic Research, Inc.
    6. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    7. repec:gam:jsusta:v:9:y:2017:i:9:p:1528-:d:110025 is not listed on IDEAS

    More about this item

    Keywords

    simultaneous treatment effects; causality; identification; do-calculus; directed acyclic graphs;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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