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Ensembling MML Causal Discovery
2004This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with ...
Honghua Dai 0001 +2 more
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Information-Theoretic Causal Discovery
2021It is well-known that correlation does not equal causation, but how can we infer causal relations from data? Causal discovery tries to answer precisely this question by rigorously analyzing under which assumptions it is feasible to infer causal networks from passively collected, so-called observational data. Particularly, causal discovery aims to infer
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A Survey on Causal Discovery: Theory and Practice
International Journal of Approximate Reasoning, 2022Fabio Antonio Stella, Alessio Zanga
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D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
ACM Computing Surveys, 2023Necati Cihan CAMGÖZ +2 more
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Disentangling causality: assumptions in causal discovery and inference
Artificial Intelligence Review, 2023Back Thomas
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Large-scale chemical process causal discovery from big data with transformer-based deep learning
Chemical Engineering Research and Design, 2023Deyang Wu
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Causal inference in drug discovery and development
Drug Discovery Today, 2023Jitao David Zhang, Tom Michoel
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Causal discovery and the problem of psychological interventions
New Ideas in Psychology, 2020Markus Ilkka Eronen
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Causal Discovery with Attention-Based Convolutional Neural Networks
Machine Learning and Knowledge Extraction, 2019Meike Nauta +2 more
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