Results 11 to 20 of about 155,351 (242)
Diffusion-Based Causal Representation Learning
EntropyCausal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause–effect estimation and the identification of efficient and safe interventions.
Amir Mohammad Karimi Mamaghan +4 more
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Toward Causal Representation Learning [PDF]
Proceedings of the IEEE, 2021 ISSN:1558 ...
Bernhard Scholkopf +6 more
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Causal imprinting in causal structure learning [PDF]
Cognitive Psychology, 2012 Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed ...
Eric G, Taylor, Woo-Kyoung, Ahn
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Learning to Learn Causal Models [PDF]
Cognitive Science, 2010 AbstractLearning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered.
Charles, Kemp +2 more
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Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning
Ocean-Land-Atmosphere Research, 2023 It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness, nonquantitativeness, computational inefficiency, etc.
Xin‐Zhong Liang +2 more
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Learning causality with graphs
AI Magazine, 2022 AbstractRecent years have witnessed a rocketing growth of machine learning methods on graph data, especially those powered by effective neural networks. Despite their success in different real‐world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, but lack consideration of causality. Causal inference can
Jing Ma, Jundong Li
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Causal Inference Gates Corticostriatal Learning [PDF]
The Journal of Neuroscience, 2020 AbstractAttributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the ...
Hayley M. Dorfman +5 more
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Multiscale Causal Structure Learning
, 2022 The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE
Gabriele D'Acunto +2 more
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Causal Structure Learning in Continuous Systems
Frontiers in Psychology, 2020 Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e., those that have been mapped to binary variables and abstracted from time.
Zachary J. Davis +2 more
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The Paradox of Time in Dynamic Causal Systems
Entropy, 2022 Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study, we build on this work by investigating the role of time in dynamic systems, where causes take continuous ...
Bob Rehder +2 more
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