Partial identification and robust treatment choice: An application to young offenders

Journal of Statistical Theory and Practice

Volumn 3, Issue 1, 2009, Pages 239-254

  Stoye, Jõrg ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

Bounds; Minimax regret; Partial identification; Statistical decision rules; Treatment choice; Treatment evaluation

Indexed keywords

EID: 85008815736     PISSN: 15598608     EISSN: 15598616     Source Type: Journal    
DOI: 10.1080/15598608.2009.10411923     Document Type: Article

References (25)

1. Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis. 2nd edition. Springer Verlag, New York.
2. Eozenou, P., Rivas, J., Schlag, K.H., 2006. Minimax regret in practice – four examples on treatment choice. preprint, European University Institute, Florence.
3. Fan, Y., Park, S., 2008. Confidence sets for some partially identified parameters. preprint, Vanderbilt University, Nashville.
4. Heckman, J.J., Vytlacil, E.J., 2007. Econometric evaluation of social programs, Part I: causal models, structural models, and econometric policy evaluation. In Handbook of Econometrics, Heckman, J.J., Leamer, E.E. (editors), Vol. 6B, 4779–4874. Elsevier, Amsterdam.
5. Hirano, K., Porter, J.R., 2008. Asymptotics for statistical treatment rules. preprint, University of Arizona, Tucson.
6. Holland, P.W., 1986. Statistics and causal inference. Journal of the American Statistical Association 81, 945–960.
7. Imbens, G., Manski, C.F., 2004. Confidence intervals for partially identified parameters. Econometrica 72, 1845–1857.
8. Manski, C.F., 1990. Nonparametric bounds on treatment effects. American Economic Review (Papers and Proceedings) 80, 319–323.
9. Manski, C.F., 2003. Partial Identification of Probability Distributions. Springer, New York.
10. Manski, C.F., 2004. Statistical treatment rules for heterogeneous populations. Econometrica 72, 1221–1246.
11. Manski, C.F., 2007a. Minimax-Regret treatment choice with missing outcome data. Journal of Econometrics 139, 105–115.
12. Manski, C.F., 2007b. Identification for Prediction and Decision. Harvard University Press, Cambridge, MA. Manski, C.F., 2008a. Diversified treatment under ambiguity. preprint, Northwestern University, Evanston.
13. Manski, C.F., Nagin, D.S., 1998. Bounding disagreements about treatment effects: a case study of sentencing and recidivism. Sociological Methodology 29, 99–137.
14. Manski, C.F., Pepper, J.V., 2000. Monotone instrumental variables: with an application to the returns to schooling. Econometrica 68, 997–1010.
15. Moon, H.R., Schorfheide, F., 2009. Bayesian and frequentist inference in partially identified models. preprint, University of Southern California and University of Pennsylvania, Los Angeles and Philadelphia.
16. Rosenbaum, P.R., 2002. Observational Studies. 2nd edition. Springer.
17. Rubin, D.B., 2005. Causal inference using potential outcomes: design, modeling, decisions. Journal of the American Statistical Association 100, 322–331.
18. Savage, L.J., 1951. The theory of statistical decisions. Journal of the American Statistical Association 46, 55–67.
19. Schlag, K.H., 2007. Eleven – designing randomized experiments under minimax regret. preprint, European University Institute, Florence.
20. Stoye, J., 2007a. Minimax regret treatment choice with incomplete data and many treatments. Econometric Theory 23, 190–199.
21. Stoye, J., 2007b. Minimax regret treatment choice with finite samples and missing outcome data. In Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications, de Cooman, G., Veinarová, J., Zaffalon, M. (editors), pp. 415–424. Prague.
22. Stoye, J., 2007c. Statistical decisions under ambiguity. preprint, New York University, New York.
23. Stoye, J., 2009a. More on confidence intervals for partially identified parameters. Econometrica, forthcoming.
24. Stoye, J., 2009b. Minimax regret treatment choice with finite samples. Journal of Econometrics, forthcoming.
25. Wasserman, L., Kadane, J.B., 1992. Computing bounds on expectations. Journal of the American Statistical Association 87, 516–522.