Theory-Based Causal Induction

Autor/inn/en	Griffiths, Thomas L.; Tenenbaum, Joshua B.
Titel	Theory-Based Causal Induction
Quelle	In: Psychological Review, 116 (2009) 4, S.661-716 (56 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0017201
Schlagwörter	Causal Models; Prior Learning; Logical Thinking; Statistical Inference; Inferences; Observation; Computation; Theories; Physics; Case Studies; Adults; Experiments + Suchen Sie Ihr Suchwort? Kausalanalyse; Vorkenntnisse; Inferential statistics; Schließende Statistik; Inference; Inferenz; Beobachtung; Theory; Theorie; Physik; Case study; Fallstudie; Case Study; Erprobung
Abstract	Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge--identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge--the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships--and show how they provide the constraints that people need to induce useful causal models from sparse data. (Contains 15 footnotes, 9 tables, and 30 figures.) (As Provided).
Anmerkungen	American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10