000021166 001__ 21166
000021166 005__ 20180122201905.0
000021166 037__ $$a379-2016-21650
000021166 041__ $$aen
000021166 245__ $$aDisproving Causal Relationships Using Observational Data
000021166 260__ $$c2006
000021166 269__ $$a2006
000021166 270__ $$mh-bryant@tamu.edu$$pBryant,   Henry L.
000021166 270__ $$md-bessler@tamu.edu$$pBessler,   David A.
000021166 270__ $$mMSHaigh@CFTC.gov$$pHaigh,   Michael S.
000021166 300__ $$a25
000021166 336__ $$aConference Paper/ Presentation
000021166 446__ $$aEnglish
000021166 490__ $$aSelected Paper
000021166 520__ $$aEconomic 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.
000021166 650__ $$aResearch Methods/ Statistical Methods
000021166 700__ $$aBryant, Henry L.
000021166 700__ $$aBessler, David A.
000021166 700__ $$aHaigh, Michael S.
000021166 8564_ $$s140615$$uhttp://ageconsearch.umn.edu/record/21166/files/sp06br03.pdf
000021166 887__ $$ahttp://purl.umn.edu/21166
000021166 909CO $$ooai:ageconsearch.umn.edu:21166$$pGLOBAL_SET
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  Previous issue date: 2006
000021166 982__ $$gAmerican Agricultural Economics Association>2006 Annual meeting, July 23-26, Long Beach, CA
000021166 980__ $$a379