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A regularity theory of causality for the social sciences

Author

Listed:
  • James Mahoney

    (Northwestern University)

  • Laura Acosta

    (Northwestern University)

Abstract

This article discusses a regularity theory of causality (RTC) for the social sciences. With RTC, causality is a relationship between X and Y characterized by three features: (1) temporal order; (2) spatiotemporal connection; and (3) constant conjunction. The article discusses each of these three features, situating them within work in the social sciences. The article explores how scholars in the fields of comparative-historical analysis (CHA) and qualitative comparative analysis (QCA) implicitly understand causality in terms of these three features. Special attention is focused on the concern of CHA with methods for establishing the spatiotemporal connection between cause and outcome. Likewise, special attention is focused on the concern of QCA with establishing constant conjunction in the form of non-spurious regularities. The article compares RTC with two other theories of causality: causal power theories, which focus on the activation of entities with generative capacities, and counterfactual theories, which view individual causes as difference-makers for outcomes. The article concludes with a call for scholars in the social sciences who implicitly use RTC to begin to do so explicitly and more self-consciously.

Suggested Citation

  • James Mahoney & Laura Acosta, 2022. "A regularity theory of causality for the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 1889-1911, August.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:4:d:10.1007_s11135-021-01190-y
    DOI: 10.1007/s11135-021-01190-y
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    References listed on IDEAS

    as
    1. Imai, Kosuke & Keele, Luke & Tingley, Dustin & Yamamoto, Teppei, 2011. "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies," American Political Science Review, Cambridge University Press, vol. 105(4), pages 765-789, November.
    2. Eva Thomann & Martino Maggetti, 2020. "Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools," Sociological Methods & Research, , vol. 49(2), pages 356-386, May.
    3. Michael Baumgartner & Alrik Thiem, 2020. "Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis," Sociological Methods & Research, , vol. 49(2), pages 279-311, May.
    4. Ragin, Charles C., 2000. "Fuzzy-Set Social Science," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226702773, December.
    5. Rodrigo Barrenechea & James Mahoney, 2019. "A Set-Theoretic Approach to Bayesian Process Tracing," Sociological Methods & Research, , vol. 48(3), pages 451-484, August.
    6. Glynn, Adam N. & Quinn, Kevin M., 2011. "Why Process Matters for Causal Inference," Political Analysis, Cambridge University Press, vol. 19(3), pages 273-286, July.
    7. Schneider, Carsten Q., 2018. "Realists and Idealists in QCA," Political Analysis, Cambridge University Press, vol. 26(2), pages 246-254, April.
    8. Thiem, Alrik, 2016. "Standards of Good Practice and the Methodology of Necessary Conditions in Qualitative Comparative Analysis," Political Analysis, Cambridge University Press, vol. 24(4), pages 478-484.
    9. repec:cup:cbooks:9780521885881 is not listed on IDEAS
    10. repec:ucp:bkecon:9780226702766 is not listed on IDEAS
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    Cited by:

    1. Dul, Jan, 2024. "A different causal perspective with Necessary Condition Analysis," Journal of Business Research, Elsevier, vol. 177(C).

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