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Proving causal relationships using observational data

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  • Bryant, Henry L.
  • Bessler, David A.

Abstract

We describe a means of rejecting a null hypothesis concerning observed, but not deliberately manipulated, variables of the form H0: A -/-> B in favor of an alternative hypothesis HA: A --> B, even given the possibility of causally related unobserved variables. Rejection of such an H0 relies on the availability of two observed and appropriately related instrumental variables. While the researcher will have limited control over the confidence level in this test, simulation results suggest that type I errors occur with a probability of less than 0.15 (often substantially less) across a wide range of circumstances. The power of the test is limited if there are but few observations available and the strength of correspondence among the variables is weak. We demonstrate the method by testing a hypothesis with critically important policy implications relating to a possible cause of childhood malnourishment.

Suggested Citation

  • Bryant, Henry L. & Bessler, David A., 2011. "Proving causal relationships using observational data," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103238, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea11:103238
    DOI: 10.22004/ag.econ.103238
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    Cited by:

    1. Nikolay Arefiev, 2016. "Graphical Interpretations of Rank Conditions For Identification of Linear Gaussian Models," HSE Working papers WP BRP 124/EC/2016, National Research University Higher School of Economics.
    2. Nikolay Arefiev, 2014. "A Theory Of Data-Oriented Identification With A Svar Application," HSE Working papers WP BRP 79/EC/2014, National Research University Higher School of Economics.

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    Research Methods/ Statistical Methods;

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