Carnegie Mellon University
Browse

Learning Deterministic Causal Networks from Observational Data

Download (514.6 kB)
journal contribution
posted on 2012-08-01, 00:00 authored by Ben Deverett, Charles KempCharles Kemp

Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We show that structure learning is successful when the causal systems in question are consistent with people’s expectations that causal relationships are deterministic and that each pattern of observations has a single underlying cause. Our data are well explained by a Bayesian model that incorporates a preference for symmetric structures and a preference for structures that make the observed data not only possible but likely

History

Publisher Statement

All Rights Reserved

Date

2012-08-01

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC