IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v5y2017i2p10n8.html
   My bibliography  Save this article

Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions

Author

Listed:
  • Pearl Judea

    (Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095-1596, USA)

Abstract

The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [1, 2] excludes disjunctive conditionals, such as “had X$X$ been x1 or x2$x_1~\mbox{or}~x_2$,” as well as disjunctive actions such as do(X=x1 or X=x2)$do(X=x_1~\mbox{or}~X=x_2)$. In contrast, the closest-world interpretation of counterfactuals (e.g. Lewis (1973a) [3]) assigns truth values to all counterfactual sentences, regardless of the logical form of the antecedent. This paper leverages “imaging” – a process of “mass-shifting” among possible worlds, to define disjunction in structural counterfactuals. We show that every imaging operation can be given an interpretation in terms of a stochastic policy in which agents choose actions with certain probabilities. This mapping, from the metaphysical to the physical, allows us to assess whether metaphysically-inspired extensions of interventional theories are warranted in a given decision making situation.

Suggested Citation

  • Pearl Judea, 2017. "Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-10, September.
    • Handle: RePEc:bpj:causin:v:5:y:2017:i:2:p:10:n:8
    DOI: 10.1515/jci-2017-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2017-0018
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2017-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hendry,David F. & Morgan,Mary S., 1997. "The Foundations of Econometric Analysis," Cambridge Books, Cambridge University Press, number 9780521588706.
    2. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bareinboim Elias & Pearl Judea, 2013. "A General Algorithm for Deciding Transportability of Experimental Results," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 107-134, June.
    2. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    3. Hsiang-Ke Chao, 2007. "A structure of the consumption function," Journal of Economic Methodology, Taylor & Francis Journals, vol. 14(2), pages 227-248.
    4. Krzyżanowski, Julian T., 2017. "The Standard Model of Trade and the Marshall – Lerner Condition," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 17(32, Part ), December.
    5. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    6. Stan A Du Plessis, 2005. "Exogeneity In A Recent Exchange Rate Model: A Response To Macdonald And Ricci," South African Journal of Economics, Economic Society of South Africa, vol. 73(4), pages 741-746, December.
    7. Kevin D. Hoover, "undated". "Econometrics And Reality," Department of Economics 97-28, California Davis - Department of Economics.
    8. Christopher L. Gilbert & Duo Qin, 2005. "The First Fifty Years of Modern Econometrics," Working Papers 544, Queen Mary University of London, School of Economics and Finance.
    9. Robert W. Dimand, 2012. "The Roots of the Present are in the Past: The Relation of Postwar Developments in Macroeconomics to Interwar Business Cycle and Monetary Theory," Chapters, in: Thomas Cate (ed.), Keynes’s General Theory, chapter 5, Edward Elgar Publishing.
    10. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    11. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    12. Duo Qin, 2014. "Inextricability of Autonomy and Confluence in Econometrics," Working Papers 189, Department of Economics, SOAS University of London, UK.
    13. Bardsen, Gunnar & Eitrheim, Oyvind & Jansen, Eilev S. & Nymoen, Ragnar, 2005. "The Econometrics of Macroeconomic Modelling," OUP Catalogue, Oxford University Press, number 9780199246502.
    14. Aurélien Goutsmedt & Erich Pinzon-Fuchs & Matthieu Renault & Francesco Sergi, 2015. "Criticizing the Lucas Critique: Macroeconometricians' Response to Robert Lucas," Post-Print halshs-01179114, HAL.
    15. Kevin D. Hoover & Òscar Jordà, 2001. "Measuring systematic monetary policy," Review, Federal Reserve Bank of St. Louis, vol. 83(Jul), pages 113-144.
    16. Morgan, Mary S., 2019. "Recovering Tinbergen," LSE Research Online Documents on Economics 101409, London School of Economics and Political Science, LSE Library.
    17. 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.
    18. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    19. Chen, Pu & Chihying, Hsiao, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 1, pages 1-43.
    20. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:causin:v:5:y:2017:i:2:p:10:n:8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.