Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robust...Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robustness.Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience.By replacing the correlation model with a stable and interpretable causal model,it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations.In this survey,we provide a comprehensive and structured review of causal inference methods in deep learning.Brain-like inference ideas are discussed from a brain-inspired perspective,and the basic concepts of causal learning are introduced.The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning.The current limitations of causal inference and future research directions are discussed.Moreover,the commonly used benchmark datasets and the corresponding download links are summarized.展开更多
We are delighted to announce the release of the first Chinese book on Mendelian randomisation,translated from Mendelian Randomization:Methods for Causal Inference Using Genetic Variants(Second Edition)by Dr Stephen Bu...We are delighted to announce the release of the first Chinese book on Mendelian randomisation,translated from Mendelian Randomization:Methods for Causal Inference Using Genetic Variants(Second Edition)by Dr Stephen Burgess and Professor Simon G Thompson,on 25 September 2023.展开更多
基金supported in part by the Key Scientific Technological Innovation Research Project of the Ministry of Education,the Joint Funds of the National Natural Science Foundation of China(U22B2054)the National Natural Science Foundation of China(62076192,61902298,61573267,61906150,and 62276199)+2 种基金the 111 Project,the Program for Cheung Kong Scholars and Innovative Research Team in University(IRT 15R53)the Science and Technology Innovation Project from the Chinese Ministry of Education,the Key Research and Development Program in Shaanxi Province of China(2019ZDLGY03-06)the China Postdoctoral Fund(2022T150506).
文摘Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robustness.Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience.By replacing the correlation model with a stable and interpretable causal model,it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations.In this survey,we provide a comprehensive and structured review of causal inference methods in deep learning.Brain-like inference ideas are discussed from a brain-inspired perspective,and the basic concepts of causal learning are introduced.The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning.The current limitations of causal inference and future research directions are discussed.Moreover,the commonly used benchmark datasets and the corresponding download links are summarized.
文摘We are delighted to announce the release of the first Chinese book on Mendelian randomisation,translated from Mendelian Randomization:Methods for Causal Inference Using Genetic Variants(Second Edition)by Dr Stephen Burgess and Professor Simon G Thompson,on 25 September 2023.
