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Disproving Causal Relationships Using Observational Data

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

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.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 71 (2009)
Issue (Month): 3 (06)
Pages: 357-374

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Handle: RePEc:bla:obuest:v:71:y:2009:i:3:p:357-374

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  1. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(01), pages 69-77, February.
  2. Kevin Hoover & Selva Demiralp & Stephen J. Perez, 2006. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Working Papers, University of California, Davis, Department of Economics 614, University of California, Davis, Department of Economics.
  3. Peltzman, Sam, 1975. "The Effects of Automobile Safety Regulation," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 83(4), pages 677-725, August.
  4. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521452175.
  5. 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, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
  6. Swanson, N.R. & Granger, C.W.J., 1994. "Impulse Response Functions Based on Causal Approach to Residual Orthogonalization in Vector Autoregressions," Papers, Pennsylvania State - Department of Economics 9-94-1, Pennsylvania State - Department of Economics.
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Cited by:
  1. 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.
  2. David Bessler & Zijun Wang, 2012. "D-separation, forecasting, and economic science: a conjecture," Theory and Decision, Springer, Springer, vol. 73(2), pages 295-314, August.

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