A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Qualitative analysis of MOS circuits
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Theories of causal ordering: reply to de Kleer and Brown
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Causal Mechanism-based Model Constructions
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Caveats for causal reasoning with equilibrium models
Caveats for causal reasoning with equilibrium models
Causality in Bayesian belief networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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In this paper we examine in detail the algorithm of Simon [H.A. Simon, Causal ordering and identifiability, in: W.C. Hood, T.C. Koopmans (Eds.), Studies in Econometric Method. Cowles Commission for Research in Economics, Monograph No. 14, John Wiley & Sons, Inc., New York, 1953, pp. 49-74, Chapter III], called the causal ordering algorithm (COA), used for constructing the ''causal ordering'' of a system given a complete specification of the system in terms of a set of ''structural'' equations that govern the variables in the system. This algorithm constructs a graphical characterization of the model in a form that we call a partial causal graph. Simon argued in [H.A. Simon, Causal ordering and identifiability, in: W.C. Hood, T.C. Koopmans (Eds.), Studies in Econometric Method. Cowles Commission for Research in Economics, Monograph No. 14, John Wiley & Sons, Inc., New York, 1953, pp. 49-74, Chapter III] and subsequent papers that a graph so generated explicates causal structure among variables in the model. We formalize this claim further by proving that any causal model based on a one-to-one correspondence between equations and variables must be consistent with the COA.