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A survey on causal inference for recommendation

The Innovation 2024年第2期5卷 130-144页

作者： Huishi Luo Fuzhen Zhuang Ruobing Xie Hengshu Zhu Deqing Wang Zhulin An Yongjun Xu Institute of Artificial Intelligence Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China WeChat Search Application Department TencentBeijing 100080China The Career Science Laboratory BOSS ZhipinBeijing 100028China SKLSDE School of Computer ScienceBeihang UniversityBeijing 100191China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

causal inference has recently garnered significant interest among recommender system(RS)researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields.It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation.Although there are already some valuable surveys on causal recommendations,they typically classify approaches based on the practical issues faced in RS,a classification that may disperse and fragment the uni-fied causal theories.Considering RS researchers’unfamiliarity with causality,it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective,thereby facilitating a deeper integration of causal inference in RS.This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy.First,we introduce the fundamental concepts of causal inference as the basis of the following review.Subsequently,we propose a novel theory-driven taxonomy,categorizing existing methods based on the causal theory employed,namely those based on the potential outcome framework,the structural causal model,and general counterfactuals.The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues.Finally,we highlight some promising directions for future research in this field.Representative papers and open-source resources will be progressively available at https://***/Chrissie-Law/causal-Inference-forRecommendation.

关键词： survey details causal

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Shared genetics and bidirectional causal relationships between type 2 diabetes and attention-deficit/hyperactivity disorder

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General Psychiatry 2023年第2期36卷 106-113页

作者： Ancha Baranova Vikas Chandhoke Hongbao Cao Fuquan Zhang School of Systems Biology George Mason UniversityFairfaxVirginiaUSA Research Centre for Medical Genetics MoscowRussian Federation Institute of Neuropsychiatry The Affiliated Brain Hospital of Nanjing Medical UniversityNanjingJiangsuChina Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical UniversityNanjingJiangsuChina

Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,pcausal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,pcausal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.

关键词： diabetes overlap causal

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Investigating genetic causal relationships between blood pressure and anxiety,depressive symptoms,neuroticism and subjective well-being

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General Psychiatry 2022年第5期35卷 303-308页

作者： Lei Cai Yonglin Liu Lin He Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)Collaborative Innovation Center of Genetics and DevelopmentShanghai Jiao Tong UniversityShanghaiChina Sanya Women and Children’s Hospital managed by Shanghai Children’s Medical Center SanyaChina Shanghai Center for Women and Children's Health ShanghaiChina

Background High blood pressure is a leading cardiovascular disease risk factor and considered to be associated with psychological factors.However,the causal relationships between blood pressure and anxiety,depressive symptoms,neuroticism and subjective wellbeing are not clear.Aims The current study explored the genetic causal relationships between blood pressure and anxiety,depressive symptoms,neuroticism and subjective wellbeing.Methods Mendelian randomisation(MR)analyses were performed using the generalised summary-data-based MR analysis method with eight large-scale genome-wide association study datasets for hypertension,systolic blood pressure(SBP),diastolic blood pressure(DBP),pulse pressure,anxiety,depressive symptoms,neuroticism and subjective well-being.Results A causal effect of DBP on neuroticism was found,and 1074 independent instrumental single nucleotide polymorphisms were identified by the incorporated Heterogeneity in Dependent Instruments-outlier test among the bidirectional causal relationship between blood pressure and the four psychological states.Conclusions DBP has a causal effect on neuroticism.Appropriate management of blood pressure may reduce neuroticism,neuroticism-inducing mood disorders and cardiovascular diseases.

关键词： cardiovascular causal effect

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The Substructure of Elementary Particles Demonstrated by the I-Theory

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Journal of High Energy Physics, Gravitation and Cosmology 2024年第2期10卷 469-514页

作者： H. H. Swami Isa Christophe Dumas Global Energy Parliament Trivandrum Kerala India

Present studies in physics assume that elementary particles are the building blocks of all matter, and that they are zero-dimensional objects which do not occupy space. The new I-Theory predicts that elementary particles do indeed have a substructure, three dimensions, and occupy space, being composed of fundamental particles called I-particles. In this article we identify the substructural pattern of elementary particles and define the quanta of energy that form each elementary particle. We demonstrate that the substructure comprises two classes of quanta which we call “attraction quanta” and “repulsion quanta”. We create a model that defines the rest-mass energy of each elementary particle and can predict new particles. Lastly, in order to incorporate this knowledge into the contemporary models of science, a revised periodic table is proposed.

关键词： I-Theory I-Particle causal Subtle Gross Quanta Attraction Quanta Repulsion Quanta Elementary Particles Leptons Bosons Hadron Periodic Table Black Matter White Matter Red Matter Gravitation Strong Force Weak Force Quantum Theory Heat Quantum Photon Neutrino

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An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony,and causal Computation

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 865-893页

作者： Lei Xu IEEE the Center for Cognitive Madmnes and Computational Health(CMaCH) School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Neural Computation Research Centre in Brain and Intelligence Science-Technology Institute Shanghai ZhangJiang National Lab

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that ar

关键词： Autoencoder LMSER duality outward attention associative recall concept formation imagining pattern transformation STD vs LTD RPCL skip connection feedback variational least redundancy Bayesian Ying Yang IA system best harmony best matching image thinking rational thinking intelligence potential theory Alpha-TSP Alpha-AGM graph matching ME Player BYY Follower constraint satisfaction causal potential theory

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Identifiability of causal effect for a simple causal model

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Science China Mathematics 2002年第3期45卷 335-341页

作者：郑忠国童行伟张艳艳 Dept. of Probability and Statistics Peking University Beijing 100871 China Tianjin Normal University Tianjin 301700 China

Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability,under which the causal efiects are identifiable.

关键词： directed acyclic graph, intervention, causal effects, ancillary information, ignorability,replaceability.

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Automorphism groups of causal symmetric spaces of Cayley type and bounded symmetric domains

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Science China Mathematics 2005年第Z1期48卷 283-294页

作者： Soji Kaneyuki Department of Mathematics Nihon Institute of Technology Miyashiro-choSaitama 345-8501Japan

Symmetric spaces of Cayley type are a higher dimensional analogue of a onesheeted hyperboloid in R3. They form an important class of causal symmetric spaces. To a symmetric space of Cayley type M, one can associate a bounded symmetric domain of tube type D. We determine the full causal automorphism group of M. This clarifies the relation between the causal automorphism group and the holomorphic automorphism group of D.

关键词： causal structure, symmetric space of Cayley type, bounded symmetric domain.

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Multi-agent modeling and analysis of EV users' travel willingness based on an integrated causal/statistical/behavioral model

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Journal of Modern Power Systems and Clean Energy 2018年第6期6卷 1255-1263页

作者： Juai WU Yusheng XUE Dongliang XIE Kang LI Fushuan WEN Junhua ZHAO Guangya YANG Qiuwei WU Nanjing University of Science & Technology State Grid Electric Power Research Institute Queen’s University Belfast Northern Ireland UK College of Electrical Engineering Zhejiang University Chinese University of Hong Kong Department of Electrical Engineering Center for Electric Power and Energy Technical University of Denmark

An electric vehicle(EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users' travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics(EE)-based simulation method can be used to analyze thebehaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EEbased simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users' travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users' travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine(ICE) vehicle users.

关键词： Travel willingness Questionnaire design Multi-agent Experimental economics causal analysis

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Relationship of causal effects in a causal chain and related inference

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Science China Mathematics 2004年第5期47卷 730-740页

作者： GENG Zhi HE Yangbo WANG Xueli School of Mathematical Sciences LMAM Peking University Beijing 100871 China School of Mathematical Sciences LMAM Peking University Beijing 100871 China School of Mathematical Sciences LMAM Peking University Beijing 100871 China

This paper discusses the relationship among the total causal effect and local causal effects in a causal chain and identifiability of causal effects. We show a transmission relationship of causal effects in a causal chain. According to the relationship, we give an approach to eliminating confounding bias through controlling for intermediate variables in a causal chain.

关键词： causal chain, causal inference, counterfactual model, graphical model, potential causal function.

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ICA Based Identification of Time-Varying Linear causal Model

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Journal of Harbin Institute of Technology(New Series) 2019年第4期26卷 32-40页

作者： Hongxia Chen Jimin Ye School of Mathematics and Statistics Xidian University

Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.

关键词： time-varying causal model independent component analysis(ICA) granger causality test causality inference

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