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

Author

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

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

  • Fréchette, Guillaume R & Vespa, Emanuel & Yuksel, Sevgi, 2025. "Extracting Statistical Relationships from Observational Data: Predicting with Full or Partial Information," University of California at San Diego, Economics Working Paper Series qt57x6d5sw, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt57x6d5sw
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    References listed on IDEAS

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    1. Sandro Ambuehl & Heidi C. Thysen, 2024. "Choosing between Causal Interpretations: An Experimental Study," CESifo Working Paper Series 11103, CESifo.
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    Cited by:

    1. Jeanne Hagenbach & Aurélien Salas, 2024. "Strategic Information Disclosure to Classification Algorithms: An Experiment," Sciences Po Economics Publications (main) hal-04823243, HAL.

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