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Extracting Statistical Relationships from Observational Data: Predicting with Full or Partial Information

Author

Listed:
  • Guillaume R. Fréchette
  • Emanuel Vespa
  • Sevgi Yuksel

Abstract

Decision-makers sometimes rely on past data to learn statistical relationships between variables. However, when predicting a target variable, they must adjust how they aggregate past information depending on the observables available. If agents have information on all observables, it is optimal to understand how the observables jointly predict the target, while with only one observable, they should focus on the unconditional correlation. An experiment examining this process shows that predictions that require the use of unconditional correlations are more challenging for decision-makers.

Suggested Citation

  • Guillaume R. Fréchette & Emanuel Vespa & Sevgi Yuksel, 2025. "Extracting Statistical Relationships from Observational Data: Predicting with Full or Partial Information," AEA Papers and Proceedings, American Economic Association, vol. 115, pages 637-642, May.
    • Handle: RePEc:aea:apandp:v:115:y:2025:p:637-42
    DOI: 10.1257/pandp.20251108
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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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