Above are papers related to the following paper: [Paper_ID] Paper #51052. 	[Title] A Statistical Semantics for Causation Key words: causality, induction, learning [Abstract] We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covari-ations following standard norms of inductive reasoning. We also establish a complete characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51045. 	[Title] Regression Can Build Predictive Causal Models [Abstract] Covariance information can help an algorithm search for predictive causal models and estimate the strengths of causal relationships. This information should not be discarded after conditional independence constraints are identified, as is usual in contemporary causal induction algorithms. Our fbd algorithm combines covariance information with an effective heuristic to build predictive causal models. We demonstrate that fbd is accurate and efficient. In one experiment we assess fbd's ability to find the best predictors for variables; in another we compare its performance, using many measures, with Pearl and Verma's ic algorithm. And although fbd is based on multiple linear regression, we cite evidence that it performs well on problems that are very difficult for regression algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #242663. 	[Title] Two Algorithms for Inducing Structural Equation Models from Data [Abstract] We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated by linear multiple regression. Our algorithms are comparable with PC [15] and IC [12, 11], which rely on conditional independence. We present the algorithms and empirical comparisons with PC and IC. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51049. 	[Title] Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology, [Abstract] Lipid Research Clinic Program 84] Lipid Research Clinic Program. The Lipid Research Clinics Coronary Primary Prevention Trial results, parts I and II. Journal of the American Medical Association, 251(3):351-374, January 1984. [Pearl 93] Judea Pearl. Aspects of graphical models connected with causality. Technical Report R-195-LL, Cognitive Systems Laboratory, UCLA, June 1993. Submitted to Biometrika (June 1993). Short version in Proceedings of the 49th Session of the International Statistical Institute: Invited papers, Flo rence, Italy, August 1993, Tome LV, Book 1, pp. 391-401. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39890. 	[Title] A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task [Abstract] This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we ad dress the issue of non-temporal causation. Please summarise all the given information.

Paper #51052 has the title `A Statistical Semantics for Causation Key words: causality, induction, learning  
` and the abstract `We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covari-ations following standard norms of inductive reasoning. We also establish a complete characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above.
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Question: Which category should Paper #51052 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #51052 has the title `A Statistical Semantics for Causation Key words: causality, induction, learning  
` and the abstract `We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covari-ations following standard norms of inductive reasoning. We also establish a complete characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above.
`.
Question: Which category should Paper #51052 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods