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Causality fields in nonlinear causal effect analysis Correspondence
Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI,dcsliuli@cqu.edu.cn,jiaoyun@hfut.edu.cn,wangaiguo2546@163.com,llian@hfut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1277-1286 doi: 10.1631/FITEE.2200165
Keywords： 非线性因果效应；因果域；z-特异性因果效应；正向因果；负向因果；空因果
Causal Inference Review
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Engineering 2020, Volume 6, Issue 3, Pages 253-263 doi: 10.1016/j.eng.2019.08.016
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop eXplainable Artificial Intelligence (XAI) algorithms is one of key steps towards to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning
and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: "Estimating average treatment effect: A brief review and beyond" from Dr. Kun Kuang, "Attribution problems in counterfactual inference" from Prof. Lian Li, "The Yule-Simpson paradox and the surrogate paradox" from Prof. Zhi Geng, "Causal potential theory" from Prof. Lei Xu, "Discovering causal information from observational data" from Prof. Kun Zhang, "Formal argumentation in causal reasoning and explanation" from Profs. Beishui Liao and Huaxin Huang, "Causal inference with complex experiments" from Prof. Peng Ding, "Instrumental variables and negative controls for observational studies" from Prof. Wang Miao, and "Causal inference with interference" from Dr. Zhichao Jiang.
Keywords： Causal inference Instructive variables Negative control Causal reasoning and explanation Causal discovery Counter factual inference Treatment effect estimation
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Strategic Study of CAE 2000, Volume 2, Issue 5, Pages 44-50
New framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown in this paper. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causal model are brought forward. On this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based on fuzzy state describing is brought forward. At the end of this paper its application in the development of intelligent controller is discussed.
Keywords： language field language value structure generalized cell automation generalized inductive logic causal model automatic reasoning intelligent controller
Xiaoni Meng, Weijie Cao, Di Liu, Isinta Maranga Elijah, Weijia Xing, Haifeng Hou, Xizhu Xu, Manshu Song, Youxin Wang
Engineering 2023, Volume 26, Issue 7, Pages 74-88 doi: 10.1016/j.eng.2022.11.004
Bidirectional causalityAlthough the association between immunoglobulin G (IgG) N-glycosylation and metabolic traits has been previously identified, the causal association between them remains unclear. In this work, we used Mendelian randomization (MR) analysis to integrate genome-wide association studies (GWASs) and quantitative trait loci (QTLs) data in order to investigate the bidirectional causal association of IgG Nglycosylation with metabolic traits. In the forward MR analysis, 59 (including nine putatively causal glycan peaks (GPs) for body mass index (BMI) (GP1, GP6, etc.) and seven for fasting plasma glucose (FPG) (GP1, GP5, etc.)) and 15 (including five putatively causal GPs for BMI (GP2, GP11, etc.) and four for FPG (GP1, GP10, etc.)) genetically determined IgG N-glycans were identified as being associated with metabolic traits in one- and two-sample MR studies, respectively, by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits (all P < 0.05). Accordingly, in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits, 72 (including one putatively causal metabolic trait for GP1 (high-density lipoprotein cholesterol (HDL-C)) and five for GP2 (FPG, systolic blood pressure (SBP), etc.)) and four (including one putatively causal metabolic trait for GP3 (HDL-C) and one for GP9 (HDL-C)) genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one- and two-sample MR studies, respectively (all P < 0.05). Notably, genetically determined associations of GP11 → BMI (fixed-effects model-Beta with standard error (SE): 0.106 (0.034) and 0.010 (0.005)) and HDL-C → GP9 (fixed-effects model-Beta with SE: –0.071 (0.022) and –0.306 (0.151)) were identified in both the one- and two-sample MR settings, which were further confirmed by a meta-analysis combining the one- and two-sample MR results (fixed-effects model-Beta with 95% confidence interval (95% CI): 0.0109 (0.0012, 0.0207) and –0.0759 (–0.1186, –0.0332), respectively). In conclusion, the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits, possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits.
Keywords： Mendelian randomization study Immunoglobulin G N-glycosylation Metabolic traits Quantitative trait loci Bidirectional causality
Dynamic relationship analysis among parties of the low-carbon building
Liu Hongyong,Zheng Junwei,Lin Cheng
Strategic Study of CAE 2012, Volume 14, Issue 12, Pages 94-99
According to the definition of academic circles about low carbon building, the definition of this paper are made from the special aspect and general aspect combined with the understanding of the energy saving building and low carbon building. It systematically analyzes the stakeholders based on the perspective of life cycle, including government, developer, design organization, construction organization, provider, consultation unit, technology development institution, evaluation testing institution, department of property management and public, puts forward to the diagram of the relationships among the stakeholders, and designs the causal dynamic relationship diagram about each stage, which are decision making stage, design stage, construction stage, operation stage and scrap processing stage, and analyzes the dynamic relationship among the parties.
Keywords： low-carbon building stakeholders causal relation diagram dynamic analysis
Yan-Ning Sun,Wei Qin,Jin-Hua Hu,Hong-Wei Xu,Poly Z.H. Sun
Engineering 2023, Volume 22, Issue 3, Pages 82-93 doi: 10.1016/j.eng.2022.06.019
The soft sensing of key performance indicators (KPIs) plays an essential role in the decision-making of complex industrial processes. Many researchers have developed data-driven soft sensors using cutting-edge machine learning (ML) or deep learning (DL) models. Moreover, feature selection is a crucial issue because a raw industrial dataset is usually high-dimensional, and not all features are conducive to the development of soft sensors. A perfect feature-selection method should not rely on hyperparameters and subsequent ML or DL models. Rather, it should be able to automatically select a subset of features for soft sensor modeling, in which each feature has a unique causal effect on industrial KPIs. Therefore, this study proposes a causal model-inspired automatic feature-selection method for the soft sensing of industrial KPIs. First, inspired by the post-nonlinear causal model, we integrate it with information theory to quantify the causal effect between each feature and the KPIs in the raw industrial dataset. After that, a novel feature-selection method is proposed to automatically select the feature with a non-zero causal effect to construct the subset of features. Finally, the constructed subset is used to develop soft sensors for the KPIs by means of an AdaBoost ensemble strategy. Experiments on two practical industrial applications confirm the effectiveness of the proposed method. In the future, this method can also be applied to other industrial processes to help develop more advanced data-driven soft sensors.
Keywords： Big data analytics Machine intelligence Quality prediction Soft sensors Intelligent manufacturing
混合-增强智能：协作与认知 Review
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 153-179 doi: 10.1631/FITEE.1700053
Qiong Zhang, Christine Prouty, Julie B. Zimmerman, James R. Mihelcic
Engineering 2016, Volume 2, Issue 4, Pages 481-489 doi: 10.1016/J.ENG.2016.04.010
The 2030 Agenda for Sustainable Development outlines 17 individual Sustainable Development Goals (SDGs) that guide the needs of practice for many professional disciplines around the world, including engineering, research, policy, and development. The SDGs represent commitments to reduce poverty, hunger, ill health, gender inequality, environmental degradation, and lack of access to clean water and sanitation. If a typical reductionist approach is employed to address and optimize individual goals, it may lead to a failure in technological, policy, or managerial development interventions through unintended consequences in other goals. This study uses a systems approach to understand the fundamental dynamics between the SDGs in order to identify potential synergies and antagonisms. A conceptual system model was constructed to illustrate the causal relationships between SDGs, examine system structures using generic system archetypes, and identify leverage points to effectively influence intentional and minimize unintentional changes in the system. The structure of interactions among the SDGs reflects three archetypes of system behavior: Reinforcing Growth, Limits to Growth, and Growth and Underinvestment. The leverage points identified from the conceptual model are gender equality, sustainable management of water and sanitation, alternative resources, sustainable livelihood standards, and global partnerships. Such a conceptual system analysis of SDGs can enhance the likelihood that the development community will broaden its understanding of the potential synergistic benefits of their projects on resource management, environmental sustainability, and climate change. By linking the interactions and feedbacks of those projects with economic gains, women’s empowerment, and educational equality, stakeholders can recognize holistic improvements that can be made to the quality of life of many of the world’s poor.
Keywords： Systems thinking Sanitation Environmental protection Gender Resource recovery Causal loop diagram Sustainability
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense Feature Article
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Engineering 2020, Volume 6, Issue 3, Pages 310-345 doi: 10.1016/j.eng.2020.01.011
Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace “dark” humanlike common sense for solving novel tasks.
Keywords： Computer vision Artificial intelligence Causality Intuitive physics Functionality Perceived intent Utility
Nonlinear Size-dependent Study of Ultra-thin Elastic Film With Surface Effect
Huang Dianwu,Li Yuanjun,Li Kai
Strategic Study of CAE 2006, Volume 8, Issue 4, Pages 54-59
A new nano-scale plate-like model in which the influence of surface effect and the geometrically nonlinear condition are considered is introduced on the basis of Mindlin theory. By using Hamilton's principle, the governing equations are derived. The residue membrane force and bending moment, which are caused by the surface stresses, are explicitly expatiated. After analyzing the membrane force and bending moment, it can be obtained that they are dependent on the deformation of the film and are accordant to classical plate theory. The model is then applied to analyze the bending and buckling of simply supported micro- and nano-films in plane strains . Differing from the conventional plate theory, the proposed model and solutions involve the intrinsic scale and depend on the thickness of the film. Thus, it can be found that when the thickness of,the film is equal to or less than the intrinsic scale, the surface effect is strongly sensitive to the thickness of the film.
Keywords： thin elastic film geometrically nonlinear surface effect intrinsic scale size-dependence
Waves of Probability and the Problems of Torsion for Quantum Effect
OuYang Shoucheng,Li Zhilan,Yuan Dongsheng
Strategic Study of CAE 2005, Volume 7, Issue 6, Pages 1-6
In this paper, the numerical experiment is conducted for Schrödinger's equivalent equation with third order derivative for nonlinear variable. The results show that the probability of probability waves is the quasi-regular flow under given condition, and it's a result of comprehensive interactions among intensity of potential field and particle density and torsion (spin) field with the quantum effect. The pure quantum effect is represented only by irregular flow.
Keywords： torsion (spin) probability quantum effect nonlinear instability curvature space
Human Prostate-Specific Antigen Carries N-Glycans with Ketodeoxynononic Acid Article
Wei Wang, Tao Zhang, Jan Nouta, Peter A. van Veelen, Noortje de Haan, Theo M. de Reijke, Manfred Wuhrer, Guinevere S.M. Lageveen-Kammeijer
Engineering 2023, Volume 26, Issue 7, Pages 119-131 doi: 10.1016/j.eng.2023.02.009
Ketodeoxynononic acid (Kdn) is a rather uncommon class of sialic acid in mammals. However, associations have been found between elevated concentrations of free or conjugated Kdn in relation to human cancer progression. Hitherto, there has been a lack of conclusive evidence that Kdn occurs on (specific) human glycoproteins (conjugated Kdn). Here, we report for the first time that Kdn is expressed on prostate-specific antigen (PSA) N-linked glycans derived from human seminal plasma and urine. Interestingly, Kdn was found only in an α2,3-linkage configuration on an antennary galactose, indicating a highly specific biosynthesis. This unusual glycosylation feature was also identified in a urinary PSA cohort in relation to prostate cancer (PCa), although no differences were found between PCa and non-PCa patients. Further research is needed to investigate the occurrence, biosynthesis, biological role, and biomarker potential of both free and conjugated Kdn in humans.
Keywords： Ketodeoxynononic acid Kdn Glycosylation Prostate cancer Prostate-specific antigen
Title Author Date Type Operation
Causality fields in nonlinear causal effect analysis
Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI,dcsliuli@cqu.edu.cn,jiaoyun@hfut.edu.cn,wangaiguo2546@163.com,llian@hfut.edu.cn
Journal Article
Causal Inference
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Journal Article
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Journal Article
Bidirectional Causality Between Immunoglobulin G N-Glycosylation and Metabolic Traits: A Mendelian Randomization Study
Xiaoni Meng, Weijie Cao, Di Liu, Isinta Maranga Elijah, Weijia Xing, Haifeng Hou, Xizhu Xu, Manshu Song, Youxin Wang
Journal Article
Dynamic relationship analysis among parties of the low-carbon building
Liu Hongyong,Zheng Junwei,Lin Cheng
Journal Article
A Causal Model-Inspired Automatic Feature-Selection Method for Developing Data-Driven Soft Sensors in Complex Industrial Processes
Yan-Ning Sun,Wei Qin,Jin-Hua Hu,Hong-Wei Xu,Poly Z.H. Sun
Journal Article
Kuang Kun: Causal Inferernce（2020-4-25）
18 Apr 2022
Conference Videos
More than Target 6.3: A Systems Approach to Rethinking Sustainable Development Goals in a Resource-Scarce World
Qiong Zhang, Christine Prouty, Julie B. Zimmerman, James R. Mihelcic
Journal Article
Kuang Kun: Causal Inference(2020.4.25)
况琨(中级职称)
14 Oct 2021
Conference Videos
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Journal Article
Qin Wei: Analysis and Decision Making of Complex Industrial Systems from a Causal Perspective (2023-5-30)
13 Jun 2023
Conference Videos
Nonlinear Size-dependent Study of Ultra-thin Elastic Film With Surface Effect
Huang Dianwu,Li Yuanjun,Li Kai
Journal Article
Waves of Probability and the Problems of Torsion for Quantum Effect
OuYang Shoucheng,Li Zhilan,Yuan Dongsheng
Journal Article
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