Randomization as an Instrumental Variable
This paper discusses how randomized social experiments operate as an instrumental variable. For two types of randomization schemes, the fundamental experimental estimation equations are derived from the principle that experiments equate bias in control and experimental samples. Using conventional econometric representations, we derive the orthogonality conditions for the fundamental estimation equations. Randomization is a multiple instrumental variable in the sense that one randomization defines the parameter of interest expressed as a function of multiple endogenous variables in the conventional usage of that term. It orthogonalizes the treatment variable simultaneously with respect to the other regressors in the model and the disturbance term for the conditional population. However, conventional `structural' parameters are not in general identified by the two types of randomization schemes widely used in practice.
|Date of creation:||Sep 1995|
|Date of revision:|
|Publication status:||published as Heckman, James J. "Randomization As An Instrumental Variable," Review of Economics and Statistics, 1996, v78(2,May), 336-341.|
|Contact details of provider:|| Postal: |
Web page: http://www.nber.org
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joshua D. Angrist & Guido W. Imbens, 1991.
"Sources of Identifying Information in Evaluation Models,"
NBER Technical Working Papers
0117, National Bureau of Economic Research, Inc.
- Angrist, J.D. & Imbens, G.W., 1991. "Sources of identifying information in evaluation models," Discussion Paper 1991-42, Tilburg University, Center for Economic Research.
- Angrist, J.D. & Imbens, G.W., 1991. "Sources of Identifying Information in Evaluation Models," Papers 9142, Tilburg - Center for Economic Research.
- Angrist, J.D. & Imbens, G.W., 1991. "Sources of Identifying Information in Evaluation Models," Harvard Institute of Economic Research Working Papers 1568, Harvard - Institute of Economic Research.
- James Heckman & Jeffrey Smith & Christopher Taber, 1994.
"Accounting for Dropouts in Evaluations of Social Experiments,"
NBER Technical Working Papers
0166, National Bureau of Economic Research, Inc.
- Heckman, J. & Smith, J. & Taber, C., 1994. "Accounting for Dropouts in Evaluations of Social Experiments," University of Chicago - Economics Research Center 94-3, Chicago - Economics Research Center.
- Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0184. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.