Statistical Science

Discussion: Models as Approximations

Dalia Ghanem and Todd A. Kuffner

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Article information

Source
Statist. Sci., Volume 34, Number 4 (2019), 604-605.

Dates
First available in Project Euclid: 8 January 2020

Permanent link to this document
https://projecteuclid.org/euclid.ss/1578474026

Digital Object Identifier
doi:10.1214/19-STS756

Mathematical Reviews number (MathSciNet)
MR4048592

Citation

Ghanem, Dalia; Kuffner, Todd A. Discussion: Models as Approximations. Statist. Sci. 34 (2019), no. 4, 604--605. doi:10.1214/19-STS756. https://projecteuclid.org/euclid.ss/1578474026


Export citation

References

  • Athey, S. and Imbens, G. W. (2017). The econometrics of randomized experiments. In Handbook of Economic Field Experiments 1 73–140. Elsevier, Amsterdam.
  • Elliott, G., Ghanem, D. and Krüger, F. (2016). Forecasting conditional probabilities of binary outcomes under misspecification. Rev. Econ. Stat. 98 742–755.
  • Imbens, G. W. and Rubin, D. B. (2015). Causal Inference—for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge Univ. Press, New York.
  • Peters, J., Bühlmann, P. and Meinshausen, N. (2016). Causal inference by using invariant prediction: Identification and confidence intervals. J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 947–1012.

See also

  • Main article: Models as Approximations I: Consequences Illustrated with Linear Regression.
  • Main article: Models as Approximations II: A Model-Free Theory of Parametric Regression.