CAUSALITY MEETS DIVERSITY (23 November 2022)

Description

Our top journal is called MIS Quarterly, not Causality Quarterly. Or is it? How should we go about building and testing causal explanations in our research and how do different approaches to causality complement each other? We invited Sunil Mithas, Ling Xue, Nina Huang, and Andrew Burton-Jones as guests. They recently published an editorial on this topic and we use this opportunity to pick their brains about experiments, econometrics, counterfactual, correlational and configurational views of establishing causality.

Episode Reading List

  • Mithas, S., Xue, L., Huang, N., & Burton-Jones, A. (2022). Causality Meets Diversity in Information Systems Research. MIS Quarterly, 46(3), i-xvii.
  • Splawa-Neyman, J. (1990). On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9. Statistical Science, 5(4), 465-470.
  • Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688-701.
  • Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
  • Bunzl, M. (2004). Counterfactual History: A User’s Guide. The American Historical Review, 109(3), 845-858.
  • Mithas, S., & Krishnan, M. S. (2009). From Association to Causation via a Potential Outcomes Approach. Information Systems Research, 20(2), 295-313.
  • Huang, N., Mojumder, P., Sun, T., Lv, J., & Golden, J. M. (2021). Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request. Information Systems Research, 32(3), 914-931.
  • Xue, L., Mithas, S., & Ray, G. (2021). IT Investment Commitment and Earnings Management: Theory and Evidence. MIS Quarterly, 45(1), 193-224.
  • Goldthorpe, J. H. (2001). Causation, Statistics and Sociology. European Sociological Review, 17(1), 1-20.
  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii-xiv.
  • Tsang, E. W. K., & Williams, J. N. (2012). Generalization and Induction: Misconceptions, Clarifications, and a Classification of Induction. MIS Quarterly, 36(3), 729-748.
  • Shojaie, A., & Fox, E. B. (2022). Granger Causality: A Review and Recent Advances. Annual Review of Statistics and Its Application, 9, 289-319.
  • Armstrong, C., Kepler, J. D., Samuels, D., & Taylor, D. (2022). Causality Redux: The Evolution of Empirical Methods in Accounting Research and the Growth of Quasi-Experiments. Journal of Accounting and Economics,https://doi.org/10.1016/j.jacceco.2022.101521.
  • Angrist, J. D., & Krueger, A. B. (2001). Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments. Journal of Economic Perspectives, 15(4), 69-85.
  • Rajkumar, K., Saint-Jacques, G., Bojinov, I., Brynjolfsson, E., & Aral, S. (2022). A Causal Test of the Strength of Weak Ties. Science, 377(6612), 1304-1310.
  • Braghieri, L., Levy, R., & Makarin, A. (2022). Social Media and Mental Health. American Economic Review,forthcoming. https://doi.org/10.2139/ssrn.3919760.
  • Mithas, S., Chen, Y., Lin, Y., & Silveira, A. d. O. (2022). On the Causality and Plausibility of Treatment Effects in Operations Management Research.Production and Operations Management,https://doi.org/10.1111/poms.13863.

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