Disproving Causal Relationships Using Observational Data
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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- 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.
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- Kevin Hoover & Selva Demiralp & Stephen J. Perez, 2006.
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614, University of California, Davis, Department of Economics.
- 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, 08.
- Selva Demiralp & Kevin D. Hoover, 2003.
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- Kevin Hoover & Selva Demiralp, 2003. "Searching for the Causal Structure of a Vector Autoregression," Working Papers 33, University of California, Davis, Department of Economics.
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