A computational theory of learning causal relationships
نویسندگان
چکیده
منابع مشابه
A Computational Theory of Learning Causal Relationships
l present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples ore consistent with a general theory of causality. This article describes o learning process thot uses a general theory of causality as background knowledge. The learning pr...
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How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive...
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We report two experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a wide range of background circumstances. We demonstrate that people prefer stable causal relationships even when overall causal strength is held constant, and we show that this effect is unlikely to be driven by a causal general...
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Valiant introduces a precise computational model for concept learning. Concept learning is about learning to decide whether a certain data belongs to a certain concept (is this a table? Is there an elephant in the data?). The paper is the birth of PAC-learning (PAC = Probably Approximately Correct). What is a concept? A concept is simply a subset of some domain. A concept comes from a some conc...
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ژورنال
عنوان ژورنال: Cognitive Science
سال: 1991
ISSN: 0364-0213
DOI: 10.1016/0364-0213(91)80003-n