Statistical Science

Comment: Spherical Cows in a Vacuum: Data Analysis Competitions for Causal Inference

Miguel A. Hernán

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

Abstract

A recent data analysis competition compared the performance of several methods for causal inference from observational data. However, sound causal inference requires not only adequate data analysis techniques but also subject-matter expertise about the causal structure of the problem under study. Therefore, until a methodology is developed to combine data analysis and subject-matter knowledge, causal inference competitions may only provide advice to practitioners under ideal conditions.

Article information

Source
Statist. Sci., Volume 34, Number 1 (2019), 69-71.

Dates
First available in Project Euclid: 12 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1555056031

Digital Object Identifier
doi:10.1214/18-STS684

Mathematical Reviews number (MathSciNet)
MR3938964

Zentralblatt MATH identifier
07110675

Keywords
Causal inference data analysis competitions

Citation

Hernán, Miguel A. Comment: Spherical Cows in a Vacuum: Data Analysis Competitions for Causal Inference. Statist. Sci. 34 (2019), no. 1, 69--71. doi:10.1214/18-STS684. https://projecteuclid.org/euclid.ss/1555056031


Export citation

References

  • Greenland, S., Pearl, J. and Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology 10 37–48.
  • Hernán, M. A., Hernández-Díaz, S. and Robins, J. M. (2004). A structural approach to selection bias. Epidemiology 15 615–625.
  • Hernán, M. A., Hsu, J. and Healy, B. (2019). Data science is science’s second chance to get causal inference right. A classification of data science tasks. Chance 32 42–49.
  • Dorie et al. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statist. Sci. 34 43–68.
  • Niswander, K. R. and Gordon, M. (1972). The Collaborative Perinatal Study of the National Institute of Neurological Diseases and Stroke: The Women and Their Pregnancies. W.B. Saunders, Philadelphia, PA.
  • Pearl, J. (2011). Understanding bias amplification. Am. J. Epidemiol. 174 1223–1227.
  • Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect. Math. Model. 7 1393–1512. (Errata appeared in Comput. Math. Appl. 14(1987), 917–921).
  • Spherical cow. In Wikipedia. Retrieved November 6, 2018, from. https://en.wikipedia.org/wiki/Spherical_cow.

See also

  • Main article: Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition.