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Prediction, Diagnosis, and Causal Thinking in Forecasting

  • Hillel J. Einhorn
  • Robin M. Hogarth
Chapter

Abstract

Imagine that you lived several thousand years ago and belonged to a tribe of methodologically sophisticated cave dwellers. Your methodological sophistication is such that you have available to you all present-day means of the methodological arsenal—details of the principles of deductive logic, probability theory, access to computational equipment, and the like. However, your level of substantive knowledge lags several thousand years behind your methodological sophistication. In particular, you have little knowledge about physics, chemistry, or biology. In recent years, your tribe has noted an alarming decrease in its birth rate. Furthermore, the tribe’s statistician estimates that unless the trend is shortly reversed, extinction is a real possibility. The tribe’s chief has accordingly launched an urgent project to determine the cause of birth. You are a member of the project team and have been assured that all means, including various forms of experimentation with human subjects, will be permitted to resolve this crucial problem.

Keywords

Temporal Order Forecast Accuracy Spurious Correlation Spatial Contiguity Causal Relevance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1985

Authors and Affiliations

  • Hillel J. Einhorn
    • 1
  • Robin M. Hogarth
    • 1
  1. 1.Center for Decision Research, Graduate School of BusinessUniversity of ChicagoChicagoUSA

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