Statistical Science

Rejoinder

Guido Imbens

Full-text: Open access

Article information

Source
Statist. Sci., Volume 29, Number 3 (2014), 375-379.

Dates
First available in Project Euclid: 23 September 2014

Permanent link to this document
https://projecteuclid.org/euclid.ss/1411437518

Digital Object Identifier
doi:10.1214/14-STS496

Mathematical Reviews number (MathSciNet)
MR3264550

Zentralblatt MATH identifier
1331.62472

Citation

Imbens, Guido. Rejoinder. Statist. Sci. 29 (2014), no. 3, 375--379. doi:10.1214/14-STS496. https://projecteuclid.org/euclid.ss/1411437518


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References

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  • Gelman, A. (2009). Resolving disputes between J. Pearl and D. Rubin on causal inference. Blog post, available at http://andrewgelman.com/2009/07/05/disputes_about/.
  • Imbens, G. and Rubin, D. (1995). Comment on: “Causal diagrams in empirical research” by Judea Pearl. Biometrika 82 694–695.
  • Kitagawa, T. (2010). Testing for instrument independence in the selection model. Unpublished manuscript, Dept. Economics, Univ. College London.
  • Kitagawa, T. (2013). A bootstrap test for instrument validity in the heterogeneous treatment effect model. Unpublished manuscript, Dept. Economics, Univ. College London.
  • Pearl, J. (2013). Reflections on Heckman and Pinto’s ‘Causal analysis after Haavelmo.’ Technical Report R-420, Univ. California, Los Angeles.
  • Richardson, T. S. (1996). Models of feedback: Interpretation and discovery. Ph.D. thesis, Carnegie-Mellon Univ., Pittsburgh, PA.
  • Shadish, W., Campbell, T. and Cook, D. (2002). Experimental and Quasi-experimental Designs for Generalized Causal Inference. Houghton Mifflin, Boston, MA.
  • Swanson, S. A. and Hernán, M. A. (2013). Commentary: How to report instrumental variable analyses (suggestions welcome). Epidemiology 24 370-374.
  • Wright, S. (1921). Correlation and causation. J. Agricultural Research 20 557–585.

See also

  • Main article: Instrumental Variables: An Econometrician's Perspective.