Statistical Treatment Rules for Heterogeneous Populations: With Application to Randomized Experiments
This paper uses Wald's concept of the risk of a statistical decision function to address the question: How should sample data on treatment response be used to guide treatment choices in a heterogeneous population? Statistical treatment rules (STRs) are statistical decision functions that map observed covariates of population members and sample data on treatment response into treatment choices. I propose evaluation of STRs by their expected welfare (negative risk in Wald's terms), and I apply this criterion to compare two STRs when the sample data are generated by a classical randomized experiment. The rules compared both embody the reasonable idea that persons should be assigned the treatment with the best empirical success rate, but they differ in their use of covariate information. The conditional success (CS) rule selects treatments with the best empirical success rates conditional on specified covariates and the unconditional success (US) rule selects a treatment with the best unconditional empirical success rate. The main finding is a proposition giving finite-sample bounds on expected welfare under the two rules. The bounds, which rest on a large-deviations theorem of Hoeffding, yield explicit sample-size and distributional conditions under which the CS Rule is superior to the US rule.
|Date of creation:||May 1999|
|Publication status:||published as Manski, Charles F. "Statistical Treatment Rules For Heterogeneous Populations," Econometrica, 2004, v72(4,Jul), 1221-1246.|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
More information through EDIRC
References listed on IDEAS
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.:
- Manski, Charles F, 1990.
"Nonparametric Bounds on Treatment Effects,"
American Economic Review,
American Economic Association, vol. 80(2), pages 319-23, May.
- Manski, C.F., 1996.
"Monotone Treatment Response,"
9604, Wisconsin Madison - Social Systems.
- Charles F. Manski, 1993.
"The Mixing Problem in Program Evaluation,"
NBER Technical Working Papers
0148, National Bureau of Economic Research, Inc.
- Frank Stafford, 1985. "Income-Maintenance Policy and Work Effort: Learning from Experiments and Labor-Market Studies," NBER Chapters, in: Social Experimentation, pages 95-144 National Bureau of Economic Research, Inc.
- Dehejia, Rajeev H., 2005.
"Program evaluation as a decision problem,"
Journal of Econometrics,
Elsevier, vol. 125(1-2), pages 141-173.
- Imbens, Guido W & Angrist, Joshua D, 1994.
"Identification and Estimation of Local Average Treatment Effects,"
Econometric Society, vol. 62(2), pages 467-75, March.
- Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
- Manski, C.F. & Nagin, D.S., 1995.
"Bounding Disagreements About Treatment Effects: A Case Study of Sentencing and Recidivism,"
9526, Wisconsin Madison - Social Systems.
- Manski, C.F. & Nagin, D.S., 1996. "Bounding Disagreements About Treatment Effects: A Case Study of Sentencing and Recidivism," Working papers 9526r, Wisconsin Madison - Social Systems.
- Manski, C.F. & Sandefur, G.D. & Mclanahan, S. & Powers, D., 1990. "Alternative Estimates Of The Effect Of Family Stucture During Adolescence On Hight School Graduation," Working papers 90-31, Wisconsin Madison - Social Systems.
- Manski, C.F., 1992. "Identification Problems in the Social Sciences," Working papers 9217, Wisconsin Madison - Social Systems.
- James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
- V. Joseph Hotz & Charles H. Mullin & Seth G. Sanders, 1997. "Bounding Causal Effects Using Data from a Contaminated Natural Experiment: Analysing the Effects of Teenage Childbearing," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 575-603.
- 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.
- Manski, Charles F., 2000. "Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice," Journal of Econometrics, Elsevier, vol. 95(2), pages 415-442, April.
When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0242. 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.