The question of what constitutes a causal model is significant, as it will lead to a better understanding of the role of causality and its heuristic role in probabilistic reasoning. It has been argued that in certain cases domain specific considerations can be appealed to in the construction of more efficient causal models that are "nonstandard" in the way networks reflect anomalous correlations between nodes. For the most part, the causal assumptions generally invoked Pearl 1988, Spirtes, Glymour, Scheines 1993 do lead to systematic efficiencies based on a reduction in computational requirements for the models they produce. The goal of this paper is to uncover some of the assumptions about causality that undergird current causal models, assumptions which should be kept in mind by those invoking causal relations in the construction of discovery algorithms for causal networks.
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Complexity, ontology, and the causal Markov assumption
Losiewicz, Paul B.
Follow ACM SIGART Bulletin
, Volume 7 (4)
Association for Computing Machinery – Oct 1, 1996
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