[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/aiz/louvar/2021052.html
   My bibliography  Save this paper

Time and causality in the social sciences

Author

Listed:
  • Wunsch, Guillaume
  • Russo, Federica
  • Mouchart, Michel

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Orsi, Renzo
Abstract
This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue is whether one can derive causal ordering from time ordering. The article examines how time is taken into account in demography and in economics as examples of social sciences in which considerations about time may diverge. We then present structural causal modeling as a modeling strategy that, while not essentially based on temporal information, can incorporate it in a more or less explicit way. In particular, we argue that temporal information is useful to the extent that it is placed in a correct causal structure, thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant than the temporal order for explanatory purposes, in establishing causal ordering the researcher should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.

Suggested Citation

  • Wunsch, Guillaume & Russo, Federica & Mouchart, Michel & Orsi, Renzo, 2021. "Time and causality in the social sciences," LIDAM Reprints ISBA 2021052, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2021052
    DOI: https://doi.org/10.1177/0961463X211029488
    Note: In: Time & Society, 2021
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
    2. Guillaume Wunsch & Michel Mouchart & Federica Russo, 2018. "Causal attribution in block-recursive social systems: A structural modeling perspective," LIDAM Reprints CORE 2948, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. D. R. Cox, 1992. "Causality: Some Statistical Aspects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 155(2), pages 291-301, March.
    4. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2019. "Examining Cause-Effect Relations in the Social Sciences : A Structural Causal Modelling Approach," LIDAM Reprints ISBA 2019049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Federica Russo & Guillaume Wunsch & Michel Mouchart, 2019. "Causality in the Social Sciences: a structural modelling framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2575-2588, September.
    6. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2018. "Causal attribution in block-recursive social systems: A structural modeling perspective," LIDAM Reprints ISBA 2018017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2010. "Do we necessarily need longitudinal data to infer causal relations?," LIDAM Reprints ISBA 2010016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2019. "Examining Cause-Effect Relations in the Social Sciences A Structural Causal Modelling Approach," LIDAM Discussion Papers ISBA 2019002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Michel Mouchart & Federica Russo & Guillaume Wunsch, 2010. "Inferrig Causal Relations by Modelling Structure," Statistica, Department of Statistics, University of Bologna, vol. 70(4), pages 411-432.
    10. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1.
    11. Christophe Vandeschrick, 2001. "The Lexis diagram, a misnomer," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 4(3), pages 97-124.
    12. Júlia Mikolai & Hill Kulu, 2019. "Union dissolution and housing trajectories in Britain," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(7), pages 161-196.
    13. Odd O. Aalen & Kjetil Røysland & Jon Michael Gran & Bruno Ledergerber, 2012. "Causality, mediation and time: a dynamic viewpoint," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 831-861, October.
    14. Russell Davidson, 2013. "Time and Causality," Annals of Economics and Statistics, GENES, issue 109-110, pages 7-22.
    15. Courgeau, Daniel, 2007. "Multilevel synthesis. From the group to the individual," MPRA Paper 43189, University Library of Munich, Germany.
    16. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
    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. M. Mouchart & R. Orsi & G. Wunsch, 2020. "Causality in Econometric Modeling. From Theory to Structural Causal Modeling," Working Papers wp1143, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Zhao, Jingya & Liu, Qingchao, 2024. "Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    3. Silvia Cimino & Luca Cerniglia & Giulia Ballarotto & Eleonora Marzilli & Esterina Pascale & Claudio D’Addario & Walter Adriani & Angelo Giovanni Icro Maremmani & Renata Tambelli, 2019. "Children’s DAT1 Polymorphism Moderates the Relationship Between Parents’ Psychological Profiles, Children’s DAT Methylation, and Their Emotional/Behavioral Functioning in a Normative Sample," IJERPH, MDPI, vol. 16(14), pages 1-20, July.
    4. Federica Russo & Guillaume Wunsch & Michel Mouchart, 2019. "Causality in the Social Sciences: a structural modelling framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2575-2588, September.
    5. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2017. "Causal attribution in block-recursive social sytems. A structural modeling perspective," LIDAM Discussion Papers ISBA 2017029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Eleonora Marzilli & Luca Cerniglia & Silvia Cimino & Renata Tambelli, 2022. "Internet Addiction among Young Adult University Students during the COVID-19 Pandemic: The Role of Peritraumatic Distress, Attachment, and Alexithymia," IJERPH, MDPI, vol. 19(23), pages 1-22, November.
    7. Catherine Gaumé & Guillaume Wunsch, 2010. "Self-Rated Health in the Baltic Countries, 1994–1999," European Journal of Population, Springer;European Association for Population Studies, vol. 26(4), pages 435-457, November.
    8. Lenihan, Helena & McGuirk, Helen & Murphy, Kevin R., 2019. "Driving innovation: Public policy and human capital," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    9. Mouchart, M. & Wunsch, G. & Russo, F., 2015. "The issue of control in multivariate systems, A contribution of structural modelling," LIDAM Discussion Papers ISBA 2015019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Mouchart, M. & Wunsch, G. & Russo, F., 2015. "The issue of control in multivariate systems A contribution of structural modelling," LIDAM Discussion Papers CORE 2015029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Russo, Federica & Wunsch, Guillaume & Mouchart, Michel, 2018. "Causality in the Social Sciences: A structural modelling framework," LIDAM Discussion Papers ISBA 2018027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2019. "La modelisation en sciences sociales: Incertitudes et defis," LIDAM Discussion Papers ISBA 2019003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Sonia Nawrocka & Hans De Witte & Margherita Pasini & Margherita Brondino, 2023. "A Person-Centered Approach to Job Insecurity: Is There a Reciprocal Relationship between the Quantitative and Qualitative Dimensions of Job Insecurity?," IJERPH, MDPI, vol. 20(7), pages 1-27, March.
    14. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    15. Vanessa Taylor & Sarah Ashelford & Patricia Fell & Penelope J Goacher, 2015. "Biosciences in nurse education: is the curriculum fit for practice? Lecturers' views and recommendations from across the UK," Journal of Clinical Nursing, John Wiley & Sons, vol. 24(19-20), pages 2797-2806, October.
    16. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    17. Ercan Tomakin, 2014. "Teaching English Tenses (grammar) in the Turkish Texts; A Case of Simple Present Tense: Is?l Maketi Iter," International Journal of Learning and Development, Macrothink Institute, vol. 4(1), pages 115-131, March.
    18. repec:rri:wpaper:201303 is not listed on IDEAS
    19. repec:phd:pjdevt:pjd_2009_vol__xxxvi_no__1-f is not listed on IDEAS
    20. Joseph P. Byrne & Alexandros Kontonikas & Alberto Montagnoli, 2013. "International Evidence on the New Keynesian Phillips Curve Using Aggregate and Disaggregate Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(5), pages 913-932, August.
    21. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    22. Christoph Hanck & Robert Czudaj, 2013. "Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation," Ruhr Economic Papers 0434, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

    More about this item

    Keywords

    Time ; causality ; social sciences ; demography ; economics ; structural modeling ; causal mechanism ; contents;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E71 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
    • H30 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - General
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

    Statistics

    Access and download statistics

    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:aiz:louvar:2021052. 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: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.html .

    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.