Assumptions and Interventions of Probabilistic Causal Models.

Autor/in	Yu, Chong Ho
Titel	Assumptions and Interventions of Probabilistic Causal Models.
Quelle	(2002), (19 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Beigaben	Tabellen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Tagungsbericht; Causal Models; Markov Processes; Probability; Statistical Inference; Structural Equation Models + Suchen Sie Ihr Suchwort? Kausalanalyse; Markowscher Prozess; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Inferential statistics; Schließende Statistik
Abstract	This paper asserts that causality is an intriguing but controversial topic in philosophy, statistics, and educational and psychological research. By supporting the Causal Markov Condition and the faithfulness condition, Clark Glymour attempted to draw causal inferences from structural equation modeling. According to Glymour, in order to make causal interpretations of no experimental data, the researcher must do some type of manipulation, rather than conditioning, of variables. The Causal Markov Condition and its sister, the common cause principle, provide the assumptions to structure relationships among variables in the path model and to load different variables into common latent constructs in the factor model. In addition, the faithfulness condition rules out those models in which statistical independence relations follow was a result of special coincidences among the parameter values. The arguments against these assumptions by Nancy Cartwright as well as those for the assumptions by James Woodward are evaluated in this paper. (Contains 4 figures and 24 references.) (Author/SLD)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2004/1/01