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- W1554316131 abstract "This chapter discusses statistical contingency models of causal induction have had two major advantages over their power-based rivals. First, statistics-based models are grounded in direct experience and, hence, promise to explicate the evidence and the processes responsible for acquiring cause-effect relationships from raw data. Second, statistics-based models enjoy the symbolic machinery of probability calculus, which enables researchers to posit hypotheses, communicate ideas, and make predictions with mathematical precision. In comparison, as well as skirting the issue of causal induction by presuming the preexistence of a causal structure, power-based theories have lacked an adequate formal language in which to cast assumptions, claims, and predictions. The chapter offers a formal setting, based on mechanisms, structures, and surgeries, which accommodates both the statistical and the power components of causal inference. It discusses how preexisting causal knowledge, cast qualitatively in the form of a graph, can combine with statistical data to produce new causal knowledge that is both qualitative and quantitative in nature. The explication of causal relationships in terms of mechanisms and physical laws is not meant to imply that the induction of physical laws is a solved, trivial task. It implies that the problem of causal induction, once freed of the mysteries and suspicions that normally surround discussions of causality, is formulated as part of the more familiar problem of scientific induction." @default.
- W1554316131 created "2016-06-24" @default.
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- W1554316131 date "1996-01-01" @default.
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- W1554316131 title "Structural and Probabilistic Causality" @default.
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- W1554316131 doi "https://doi.org/10.1016/s0079-7421(08)60566-6" @default.
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