Teaching the Normative Theory of Causal Reasoning
| Abstract | There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or observational study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this paper we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational. | |||||||||
| Keywords | No keywords specified (fix it) | |||||||||
| Categories | ||||||||||
| Options |
|
|||||||||
Download options| PhilPapers Archive |
Upload a copy of this paper Check publisher's policy on self-archival Papers currently archived: 5,705 |
| External links |
  Try with proxy. |
| Through your library | Only published papers are available at libraries |
Similar books and articlesRalph Wedgwood (2006). The Normative Force of Reasoning. Noûs 40 (4):660–686.
Sieghard Beller & Gregory Kuhnm (2007). What Causal Conditional Reasoning Tells Us About People's Understanding of Causality. Thinking and Reasoning 13 (4):426 – 460.
Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
Peter A. White (2011). Not by Contingency: Some Arguments About the Fundamentals of Human Causal Learning. Thinking and Reasoning 15 (2):129-166.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
York Hagmayer & Magda Osman (2012). From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning. Synthese 189 (S1):17-28.
David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
Analytics
Monthly downloads |
Added to index2010-12-22Total downloads5 ( #160,483 of 549,196 )Recent downloads (6 months)1 ( #63,397 of 549,196 )How can I increase my downloads? |
My notes
Discussion
