‘Homo economicus’ as an intuitive statistician (3): Data-driven causal inference

Reza Salehnejad

Chapter 5 in Rationality, bounded rationality and microfoundations, 2007, pp 165-203 from Palgrave Macmillan

Abstract: Abstract The bounded rationality programme, as understood in new classical economics, views the economy as a society of intuitive statisticians – the intuitive statistician hypothesis. This hypothesis raises the question of whether there is a ‘tight enough’ theory of statistical inference.Without a tight enough theory of statistical inference we will not learn much about the economy by studying the dynamics of an economy of intuitive statisticians. As a general framework for studying this question, we conjectured that the agent (statistician) first seeks to learn the probability distribution of the variables representing his or her choice situation and next uses the probabilistic information to learn about the causal structure of the situation. The last two chapters studied some of the issues relating to learning the probability distribution of a set of variables. This chapter studies in detail the second general stage of inference that is concerned with inferring the causal structure of a set of variables from their joint distribution.

Keywords: Directed Acyclic Graph; Causal Inference; Causal Model; Causal Structure; Equivalent Model (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:pal:palchp:978-0-230-62515-0_6

Ordering information: This item can be ordered from
http://www.palgrave.com/9780230625150

DOI: 10.1057/9780230625150_6

Access Statistics for this chapter

More chapters in Palgrave Macmillan Books from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().