Heterogeneous treatment effects: What does a regression estimate?

Evaluation Review

Volumn 34, Issue 4, 2010, Pages 334-361

  Rhodes, William ^a  

^a ABT ASSOCIATES   (United States)

Author keywords

Heterogeneous treatment effects; Hierarchical models; Propensity score; Random effects; Regression

Indexed keywords

ARTICLE; EPIDEMIOLOGY; EVALUATION; HUMAN; MONTE CARLO METHOD; PROPENSITY SCORE; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICS;

CONFOUNDING FACTORS (EPIDEMIOLOGY); EVALUATION STUDIES AS TOPIC; HUMANS; LINEAR MODELS; MODELS, STATISTICAL; MONTE CARLO METHOD; PROPENSITY SCORE; REGRESSION ANALYSIS; STATISTICS AS TOPIC;

EID: 77955017830     PISSN: 0193841X     EISSN: 15523926     Source Type: Journal    
DOI: 10.1177/0193841X10372890     Document Type: Article

References (41)

1. Angrist, J. 1998. Estimating the labor force impact of voluntary military service using social security data on military applications. Econometrica 66:249-88.
2. Angrist, J., and A. Krueger. 1999. Empirical strategies in labor economics. In Handbook of labor economics, ed., O. Ashenfelder and D. Card, 1sted, 1277-366. Amsterdam: Elsevier.
3. Angrist, J., and J. Pischke. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton: Princeton University Press.
4. Bang, H., and J. Robins. 2005. Doubly robust estimation in missing data and causal inference models. Biometrics 61:692-972.
5. Berk, R. 2005. Randomized experiments as the bronze standard. Journal of Experimental Criminology 1:417-33.
6. Bryk, A., and S. Raudenbush. 1992. Hierarchical linear models. Newbury Park: SAGE.
7. Burtless, G. 1995. The case for randomized field trials in econometrics and policy research. Journal of Economic Perspectives 9:63-84.
8. Cameron, A., and P. Trivedi. 2005. Microeconometrics: Methods and applications. Cambridge, UK: Cambridge University Press.
9. Crump, R., V. Hotz, G. Imbens, and O. Mitnik. 2008a. Dealing with limited overlap in estimation of average treatment effects. Biomerica 96:187-99.
10. Crump, R., V. Hotz, G. Imbens, and O. Mitnik. 2008b. Nonparametric tests for treatment effect heterogeneity. Review of Economics and Statistics 90:389-405.
11. D'Agostino, R. 1998. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine 17:2265-80.
12. Freedman, D., and R. Berk. 2008. Weighting regressions by propensity scores. Evaluation Review 32:392-409.
13. Gelman, A., and J. Hill. 2007. Data analysis: Using regression and multilevel/hierarchical models. Cambridge, UK: Cambridge University Press.
14. Greene, W. 2008. Econometric analysis, 6th ed. Upper Saddle River, NJ: Prentice Hall.
15. Heckman, J. 2000. Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture. Journal of Political Economy 109:673-748.
16. Heckman, J. 1979. Sample selection bias as a specification error. Econometrica 47:153-61.
17. Heckman, J., and V. Hotz. 1989. Choosing among alternativenonexperomental methods for evaluating the impact of social programs: The case of manpower training. Journal of the American Statistical Association 84:862-74.
18. Heckman, J., H. Ichimura, J. Smith, and P. Todd. 1998. Characterizing selection bias using experimental data. Econometrica 66:1017-98.
19. Heckman, J., and E. Vytlacil. 2007a. Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluations. In Handbook of econometrics, Vol. 6B, ed., J. Heckman and E. Leamer. Amsterdam: North Holland Press.
20. Heckman, J., and E. Vytlacil. 2007b. Econometric evaluations of social programs, Part II: Using the marginal treatment effect to evaluate social programs, and to forecast their effects in new environments. In Handbook of econometrics, Vol. 6B, ed., J. Heckman, and E. Leamer. Amsterdam: North-Holland Press.
21. Hirano, K., G. Imbens, and G. Ridder. 2003. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71:1161-89.
22. Imbens, G. 2004. Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics 86:4-29.
23. Imbens, G., and J. Wooldridge. 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47:5-86.
24. LaLonda, T. 1986. Evaluating the econometric evaluation of training programs with experimental data. American Economic Review 76:604-20.
25. Lee, M. 2005. Micro-econometrics for policy, program and treatment effects. Oxford, UK: Oxford University Press.
26. Maddala, G. 1983. Limited-dependent and qualitative variables in econometrics. Cambridge, UK: Cambridge University Press.
27. Manski, C. 2007. Identification for prediction and decision. Cambridge, MA: Harvard University Press.
28. Morgan, S., and C. Winship. 2007. Counterfactuals and causal inferences: Methods and principals for social research. Cambridge, UK: Cambridge University Press.
29. Orr, L. 1999. Social experiments: Evaluating public policy progrms with experimental methods. Thousand Oaks, CA: SAGE.
30. Pearl, J. 2000. Causality: Models, reasoning and inferences. Cambridge, UK: Cambridge University Press.
31. Rhodes, W. 2009. Estimating treatment effects: A primer for evaluators. Unpublished manuscript, Cambridge, MA.
32. Robins, J., and A. Rotnitzky. Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90:122-9.
33. Robins, J., A. Rotnitzky, and L. Zhao. 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90:106-21.
34. Rosenbaum, P. 1987. Model-based direct adjustments. Journal of the American Statistical Association 82:387-94.
35. Rosenbaum, P. 2002. Observational studies. 2nd edn. New York: Springer-Verlag.
36. Rosenbaum, P. 1997. The role of a second control group in an observational study. Statistical Science 2:292-316.
37. Rosenbaum, P., and L. Braitmen. 2002. Rare outcomes, common treatments: Analytical strategies using propensity scores. Annals of Internal Medicine 137:693-95.
38. Rosenbaum, P., and D. Rubin. 1984. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 7:516-24.
39. Roy, A. 1951. Some thoughts on the distribution of earnings. Oxford Economic Papers 3:135-46.
40. Winship, C., and S. Morgan. 1999. The estimation of causal effects from observational data. Annual Review of Sociology 25:659-706.
41. Wooldridge, J. 2007. Inverse probability weighted estimation for general missing data problems. Journal of Econometrics 141:1281-301.