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Trygve Haavelmo And The Emergence Of Causal Calculus

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  • Pearl, Judea

Abstract

Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome and lays out a logical framework that has evolved from Haavelmo’s insight and matured into a coherent and comprehensive account of the relationships between theory, data, and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, nonparametric identification, mediation analysis, and introspection. Finally, we observe that economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, to close this gap, we identify concrete recent advances in causal analysis that economists can utilize in research and education.

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  • Pearl, Judea, 2015. "Trygve Haavelmo And The Emergence Of Causal Calculus," Econometric Theory, Cambridge University Press, vol. 31(1), pages 152-179, February.
    • Handle: RePEc:cup:etheor:v:31:y:2015:i:01:p:152-179_00
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    Citations

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    Cited by:

    1. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    2. 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.
    3. Breznau, Nate, 2017. "Simultaneous Feedback Models with Macro-Comparative Cross-Sectional Data," OSF Preprints v4sxb, Center for Open Science.
    4. LeRoy, Stephen F., 2019. "Causal Inference," University of California at Santa Barbara, Economics Working Paper Series qt6pc1x9r6, Department of Economics, UC Santa Barbara.
    5. Ali Tafti & Galit Shmueli, 2020. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure," Information Systems Research, INFORMS, vol. 31(4), pages 1183-1199, December.
    6. Stephen F. LeRoy, 2018. "Implementation-Neutral Causation in Structural Models," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(3), September.
    7. Leonardo Marinho, 2022. "Causal Impulse Responses for Time Series," Working Papers Series 570, Central Bank of Brazil, Research Department.
    8. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    9. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    10. Forney Andrew & Mueller Scott, 2022. "Causal inference in AI education: A primer," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 141-173, January.
    11. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
    12. Judea Pearl, 2017. "Detecting Latent Heterogeneity," Sociological Methods & Research, , vol. 46(3), pages 370-389, August.
    13. Liu, Ming & Liu, Zhongzheng & Chu, Feng & Dolgui, Alexandre & Chu, Chengbin & Zheng, Feifeng, 2022. "An optimization approach for multi-echelon supply chain viability with disruption risk minimization," Omega, Elsevier, vol. 112(C).
    14. Arefiev, Nikolay & Khabibullin, Ramis, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 49, pages 115-142.
    15. Pearl Judea, 2022. "Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 221-226, January.

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