Disproving Causal Relationships Using Observational Data Academic Article uri icon

abstract

  • Economic theory is replete with causal hypotheses that are scarcely tested because economists are generally constrained to work with observational data. We describe a method for testing a hypothesis that one observed random variable causes another. Contingent on a sufficiently strong correspondence between the two variables, an appropriately related third variable can be employed for the test. The logic of the procedure naturally suggests strong and weak grounds for rejecting the causal hypothesis. 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. Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009.

published proceedings

  • Oxford Bulletin of Economics and Statistics

author list (cited authors)

  • Bryant, H. L., Bessler, D. A., & Haigh, M. S.

citation count

  • 13

complete list of authors

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

publication date

  • June 2009

publisher