Statistical Science

Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments

Kosuke Imai, Gary King, and Clayton Nall

Full-text: Open access

Article information

Source
Statist. Sci. Volume 24, Number 1 (2009), 65-72.

Dates
First available in Project Euclid: 8 October 2009

Permanent link to this document
http://projecteuclid.org/euclid.ss/1255009011

Digital Object Identifier
doi:10.1214/09-STS274REJ

Mathematical Reviews number (MathSciNet)
MR2561132

Citation

Imai, Kosuke; King, Gary; Nall, Clayton. Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments. Statist. Sci. 24 (2009), no. 1, 65--72. doi:10.1214/09-STS274REJ. http://projecteuclid.org/euclid.ss/1255009011.


Export citation

References

  • Ashenfelter, O. (1978). Estimating the effect of training programs on earnings. Rev. Econ. Statist. 47–57.
  • Freedman, D. A. (2008). On regression adjustments to experimental data. Adv. in Appl. Math. 40 180–193.
  • Greevy, R., Lu, B., Silver, J. H. and Rosenbaum, P. (2004). Optimal multivariate matching before randomization. Biostatistics 5 263–275.
  • Hill, J. and Scott, M. (2009). Comment on “The essential role of pair matching.” Statist. Sci. 24 54–58.
  • Ho, D., Imai, K., King, G. and Stuart, E. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15 199–236. Available at http://gking.harvard.edu/files/abs/matchp-abs.shtml.
  • Ho, D. E., Imai, K., King, G. and Stuart, E. A. (2009). Matchit: Nonparametric preprocessing for parametric causal inference. J. Statist. Software. To appear. Available at http://gking.harvard.edu/matchit.
  • Honaker, J. and King, G. (2009). What to do about missing values in time series cross-section data. Available at http://gking.harvard.edu/files/abs/pr-abs.shtml.
  • Iacus, S. M., King, G. and Porro, G. (2008). Matching for causal inference without balance checking. Available at http://gking.harvard.edu/files/abs/cem-abs.shtml.
  • Imai, K., King, G. and Nall, C. (2009a). Replication data for: The essential role of pair-matching in cluster-randomized experiments, with application to the mexican universal health insurance evaluation: Rejoinder. Available at hdl:1902.1/12730 UNF:3:CKs4T0iVYxP36LQSMgAkuw== Murray Research Archive [Distributor].
  • Imai, K., King, G. and Nall, C. (2009b). The essential role of pair matching in cluster-randomized experiments, with application to the mexican universal health insurance evaluation. Statist. Sci. 24 29–53.
  • Imai, K., King, G. and Stuart, E. (2008). Misunderstandings among experimentalists and observationalists about causal inference. J. Roy. Statist. Soc. Ser. A 171 481–502. Available at http://gking.harvard.edu/files/abs/matchse-abs.shtml.
  • Imbens, G. (2009). Better LATE than nothing: Some comments on Deaton (2009) and Heckman and Urzua (2009). Working paper, NBER.
  • King, G., Gakidou, E., Imai, K., Lakin, J., Moore, R. T., Nall, C., Ravishankar, N., Vargas, M., Téllez-Rojo, M. M., Ávila, J. E. H., Ávila, M. H. and Llamas, H. H. (2009). Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. The Lancet. To appear. Available at http://gking.harvard.edu/files/abs/spi-abs.shtml.
  • King, G., Gakidou, E., Ravishankar, N., Moore, R. T., Lakin, J., Vargas, M., Téllez-Rojo, M. M., Ávila, J. E. H., Ávila, M. H. and Llamas, H. H. (2007). A ‘politically robust’ experimental design for public policy evaluation, with application to the Mexican universal health insurance program. J. Policy Anal. Manag. 26 479–506. Available at http://gking.harvard.edu/files/abs/spd-abs.shtml.
  • King, G. and Zeng, L. (2006). The dangers of extreme counterfactuals. Political Anal. 14 131–159. Available at http://gking.harvard.edu/files/abs/counterft-abs.shtml.
  • Rosenbaum, P. (1984). The consequences of adjusting for a concomitant variable that has been affected by the treatment. J. Roy. Statist. Soc. Ser. A 147 656–666.
  • Zhang, K. and Small, D. S. (2009). Comment on “The essential role of pair matching in cluster-randomized experiments, with application to the mexican universal health insurance program.” Statist. Sci. 24 59–64.