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Causal Modeling and the Statistical Analysis of Causation

Published online by Cambridge University Press:  31 January 2023

Gurol Irzik
Gurol Irzik*
Affiliation:
University of Southern Indiana
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Extract

Recent studies on probabilistic causation and statistical explanation (Cartwright 1979; Salmon 1984), I believe, have opened up the possibility of a genuine unification between philosophical approaches and causal modeling (CM) in the social, behavioral and biological sciences (Wright 1934; Blalock 1964; Asher 1976). This unification rests on the statistical tools employed, the principle of common cause, the irreducibility of causation to probability or statistics, and the idea of causal process as a suitable framework for understanding causal relationships. The aim of this paper is to draw attention to these four areas of contact by focusing on the relevant aspects of CM.

Causal analysis in the social sciences is based on two fundamental notions: model and method. A causal model is an idealized picture of the causal relationships in the world. Method, on the other hand, refers to certain statistical techniques that are used to evaluate and test a causal model using data which consist of joint observations on the model variables.

Type
Part I. Physics
Information
PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , Volume 1986 , Issue 1: Volume One: Contributed Papers 1986 , 1986 , pp. 12 - 23
Copyright
Copyright © Philosophy of Science Association 1986

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