Erratum and discussion of propensity-score reweighting

Stata Journal

Volumn 8, Issue 4, 2008, Pages 532-539

  Nichols, Austin ^a  

^a URBAN INSTITUTE   (United States)

Author keywords

Causal inference; Discontinuity; Excluded instrument; Instrumental; ivreg; ivreg2; ivregress; Local polynomial; lpoly; Match; Matching; nnmatch; Panel; Propensity score; psmatch2; Ranktest; rd; Regression; Reweighting; st0136_1; Variables; Weak identification; xtoverid; xtreg

Indexed keywords

EID: 58449126569     PISSN: 1536867X     EISSN: None     Source Type: Journal    
DOI: 10.1177/1536867x0800800405     Document Type: Article

References (13)

1. Altonji, J. G., P. Bharadwaj, and F. Lange. 2008. Changes in the characteristics of American youth: Implications for adult outcomes. Working paper. Yale University. http://www.econ.yale.edu/-f188/SkillComposition.pdf.
2. Bia, M., and A. Mattei. 2008. A Stata package for the estimation of the dose-response function through adjustment for the generalized propensity score. Stata Journal 8: 354-373.
3. Brunell, T. L., and J. DiNardo. 2004. A propensity score reweighting approach to estimating the partisan effects of full turnout in American presidential elections. Political Analysis 12: 28-45.
4. Busso, M., J. DiNardo, and J. McCrary. 2008. Finite sample properties of semiparametric estimators of average treatment effects. Working paper, University of Michigan. http://www-personal.umich.edu/∼jdinardo/BDM2008_v11. pdf.
5. Carlin, J. B., J. C. Galati, and P. Royston. 2008. A new framework for managing and analyzing multiply imputed data in Stata. Stata Journal 8: 49-67.
6. DiNardo, J. 2002. Propensity score reweighting and changes in wage distributions. Working Paper, University of Michigan. http://www-personal.umich. edu/∼jdinardo/bztalk5.pdf.
7. DiNardo, J., N. M. Fortin, and T. Lemieux. 1996. Labor market institutions and the distribution of wages, 1973-1992: A semiparametric approach. Econometrica 64: 1001-1044.
8. Hirano, K., and G. W. Imbens. 2004. The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, ed. A. Gelman and X.-L. Meng, 73-84. Chichester, UK: Wiley.
9. Iacus, S. M., G. King, and G. Porro. 2008. Matching for causal inference without balance checking. Downloadable from http://gking.harvard.edu/cem.
10. Jann, B. 2008. The Blinder-Oaxaca decomposition for linear regression models. Stata Journal 8: 453-479.
11. Lemieux, T. 2002. Decomposing changes in wage distributions: A unified approach. Canadian Journal of Economics 35: 646-688.
12. Lunceford, J. K., and M. Davidian. 2004. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine 23: 2937-2960.
13. Nichols, A. 2007. Causal inference with observational data. Stata Journal 7: 507-541.