Advertisement

Psychometrika

, Volume 83, Issue 4, pp 1007–1010 | Cite as

Review of Observation and Experiment: An Introduction to Causal Inference by Paul R. Rosenbaum

  • Joel B. Greenhouse
  • Edward H. Kennedy
Book Review
  • 153 Downloads
In 1922, RA Fisher set out to place the practice of statistics on a solid theoretical foundation. He wrote,

\(\ldots \)

References

  1. Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.CrossRefGoogle Scholar
  2. Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver and Boyd.Google Scholar
  3. Hernán, M. A. & Robins, J. M. (2018). https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/. Accessed 6 August 2018.
  4. Loux, M. J., & Crisp, T. M. (2017). Metaphysics: A contemporary introduction (4th ed.). New York: Routledge.CrossRefGoogle Scholar
  5. Neyman, J. (1923). On the application of probability theory to agricultural experiments, Essay on principles: Section 9, Annals of Agricultural Sciences X: 1–51 (Translated by D. M. Dabrowska and T. P. Speed (1990). Statistical Science, 5(4), 465–472).Google Scholar
  6. Rosenbaum, P. (2017). Observation and experiment: An introduction to causal inference. Cambridge: Harvard University Press.CrossRefGoogle Scholar
  7. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.CrossRefGoogle Scholar
  8. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34–58.CrossRefGoogle Scholar
  9. Weisberg, H. I. (2010). Bias and causation: Models and judgment for valid comparisons. Hoboken: Wiley.CrossRefGoogle Scholar

Copyright information

© The Psychometric Society 2018

Authors and Affiliations

  1. 1.Department of Statistics and Data ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations