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

Guillaume R Fréchette, Emanuel Vespa and Sevgi Yuksel

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

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.

Keywords: Economics; Economic Theory (search for similar items in EconPapers)
Date: 2025-05-01
New Economics Papers: this item is included in nep-for
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.escholarship.org/uc/item/57x6d5sw.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:ucsdec:qt57x6d5sw

Access Statistics for this paper

More papers in University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().