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Disproving Causal Relationships Using Observational Data

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  • Bryant, Henry L.
  • Bessler, David A.
  • Haigh, Michael S.

Abstract

Economic theory is replete with causal hypotheses that are scarcely tested because economists are generally constrained to work with observational data. This article describes the use of causal inference methods for testing a hypothesis that one random variable causes another. Contingent on a sufficiently strong correspondence between the hypothesized cause and effect, an appropriately related third variable can be employed for such a test. The procedure is intuitive, and is easy to implement. The basic logic of the procedure naturally suggests strong and weak grounds for rejecting the hypothesized causal relationship. Monte Carlo results suggest that weakly-grounded rejections are unreliable for small samples, but reasonably reliable for large samples. Strongly-grounded rejections are highly reliable, even for small samples.

Suggested Citation

  • Bryant, Henry L. & Bessler, David A. & Haigh, Michael S., 2006. "Disproving Causal Relationships Using Observational Data," 2006 Annual meeting, July 23-26, Long Beach, CA 21166, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea06:21166
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    References listed on IDEAS

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    1. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    2. Selva Demiralp & Kevin D. Hoover & Stephen J. Perez, 2008. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 509-533, August.
    3. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(01), pages 69-77, February.
    4. Swanson, N.R. & Granger, C.W.J., 1994. "Impulse Response Functions Based on Causal Approach to Residual Orthogonalization in Vector Autoregressions," Papers 9-94-1, Pennsylvania State - Department of Economics.
    5. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, March.
    6. Peltzman, Sam, 1975. "The Effects of Automobile Safety Regulation," Journal of Political Economy, University of Chicago Press, vol. 83(4), pages 677-725, August.
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    Citations

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    Cited by:

    1. Dharmasena, Senarath & Bessler, David A. & Todd, Jessica, 2016. "Socioeconomic, Demographic and Geographic Factors Affecting Household Food Purchase and Acquisition Decisions in the United States as a Complex Economic System," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235646, Agricultural and Applied Economics Association.
    2. Hogun Chong & Mary Zey & David A. Bessler, 2010. "On corporate structure, strategy, and performance: a study with directed acyclic graphs and PC algorithm," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 47-62.
    3. Michael Margolis, 2017. "Graphs as a Tool for the Close Reading of Econometrics (Settler Mortality is not a Valid Instrument for Institutions)," Economic Thought, World Economics Association, vol. 6(1), pages 56-82, March.
    4. David Bessler & Zijun Wang, 2012. "D-separation, forecasting, and economic science: a conjecture," Theory and Decision, Springer, vol. 73(2), pages 295-314, August.
    5. Xiaojie Xu, 2017. "Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs," Empirical Economics, Springer, vol. 52(2), pages 731-758, March.
    6. Henry L. Bryant & David A. Bessler, 2016. "Conditions Sufficient to Infer Causal Relationships Using Instrumental Variables and Observational Data," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 29-57, June.
    7. Anton-Erxleben, Katharina & Kibriya, Shahriar & Zhang, Yu, 2016. "Bullying as the main driver of low performance in schools: Evidence from Botswana, Ghana, and South Africa," MPRA Paper 75555, University Library of Munich, Germany.

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