Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T23:21:46.331Z Has data issue: false hasContentIssue false
  • English
  • Français

IMPLEMENTATION NEUTRALITY AND TREATMENT EVALUATION

Published online by Cambridge University Press:  26 September 2017

Stephen F. LeRoy
Stephen F. LeRoy*
Affiliation:
Department of Economics, University of California, Santa Barbara 93106, CA, USA. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract:

Statisticians have proposed formal techniques for evaluation of treatments, often in the context of models that do not explicitly specify how treatments are generated. Under such procedures they run the risk of attributing causation in settings where the implementation neutrality condition required for causal interpretation of parameter estimates is not satisfied. When treatment assignments are explicitly modelled, as economists recommend, these issues can be formally analysed, and the existence (or lack thereof) of implementation neutrality, and therefore quantifiable causation, can be determined. Examples are given.

Keywords

CausationTreatment evaluationImplementation neutralityIdentification
Type
Articles
Information
Economics & Philosophy , Volume 34 , Issue 1 , March 2018 , pp. 45 - 52
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Angrist, J. D. 1990. Lifetime earnings and the Vietnam era draft lottery. American Economic Review 80: 313–336.Google Scholar
Heckman, J. J. 2001. Econometrics Counterfactuals and Causal Models. Chicago, IL: University of Chicago.Google Scholar
LeRoy, S. F. 2016. Implementation neutrality and causation. Economics and Philosophy 32: 121–142.Google Scholar
Pearl, J. 2001. Causality. Cambridge: Cambridge University Press.Google Scholar
Rubin, D. 1974. Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology 66: 688–701.CrossRefGoogle Scholar
Rubin, D. 1978. Bayesian inference for causal effects: the role of randomization. Annals of Statistics 7: 34–58.Google Scholar
Spirtes, P., Glymour, C. and Schienes, R.. 1993. Causation, Prediction and Search. Cambridge, MA: MIT Press.Google Scholar