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IMPLEMENTATION-NEUTRAL CAUSATION

Published online by Cambridge University Press:  20 July 2015

Stephen F. LeRoy*
Affiliation:
Department of Economics, University of California, Santa Barbara, CA 93106, USA. Email: leroy@ucsb.edu. URL: econ.ucsb.edu.

Abstract:

The most basic question one can ask of a model is ‘What is the effect on variable y2 of variable y1?’ Causation is ‘implementation neutral’ when all interventions on external variables that lead to a given change in y1 have the same effect on y2, so that the effect of y1 on y2 is defined unambiguously. Familiar ideas of causal analysis do not apply when causation is implementation neutral. For example, a cause variable cannot be linked to an effect variable by both a direct path and a distinct indirect path. Discussion of empirical aspects of implementation neutrality leads to further unexpected results, such as that if one variable causes another the coefficient representing that causal link is always identified.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Cartwright, N. 2007. Hunting Causes and Using Them. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Cooley, T. F. and LeRoy, S. F.. 1985. Atheoretical macroeconometrics: a critique. Journal of Monetary Economics 16: 283308.CrossRefGoogle Scholar
Elwert, F. 2013. Graphical causal models. In Handbook of Causal Analysis for Social Research, ed. Morgan, S., 245273. New York, NY: Sage.CrossRefGoogle Scholar
Engle, R. F., Hendry, D. F. and Richard, J.-F.. 1983. Exogeneity. Econometrica 51: 277304.CrossRefGoogle Scholar
Granger, C.W. J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424438.CrossRefGoogle Scholar
Granger, C.W. J. 1995. Commentary. In Macroeconometrics: Developments, Tensions and Prospects, ed. Hoover, K. D., 229233. Amsterdam: Kluwer Academic Publishers.Google Scholar
Haavelmo, T. 1943. The statistical implications of a system of simultaneous equations. Econometrica 11:112.CrossRefGoogle Scholar
Hausman, D. M. 1998. Causal Asymmetries. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hoover, K. D. 2001. Causality in Macroeconomics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Leamer, E. E. 1985. Vector Autoregressions for Causal Inference? Carnegie-Rochester Conference Series on Public Policy, Vol. 22. Amsterdam: Elsevier.CrossRefGoogle Scholar
LeRoy, S. F. 1995. Causal orderings. In Macroeconometrics: Developments, Tensions and Prospects, ed. Hoover, K. D., 221227. Amsterdam: Kluwer Academic Publishers.Google Scholar
LeRoy, S. F. 2006. Causality in Economics. Reproduced, University of California, Santa Barbara.Google Scholar
Pearl, J. 2001. Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.Google Scholar
Simon, H. A. 1953. Causal ordering and identifiability. In Studies in Econometric Method, ed. Hood, W. C. and Koopmans, T. C., 249274. New York, NY: John Wiley and Sons.Google Scholar
Spirtes, P., Glymour, C. and Schienes, R.. 1993. Causation, Prediction and Search. New York, NY: Springer-Verlag.CrossRefGoogle Scholar
Strotz, R. H. and Wold, H. O. A.. 1960. Recursive versus nonrecursive systems: an attempt at synthesis. Econometrica 28: 417427.CrossRefGoogle Scholar
Woodward, J. 2003. Making Things Happen. Oxford: Oxford University Press.Google Scholar
Woodward, J. 2007. Causation with a human face. In Causation, Physics and the Constitution of Reality: Russell's Republic Revisited, ed. Price, H. and Corry, R., 266305. Oxford: Oxford University Press.Google Scholar