{"type":"ScholarlyArticle","title":"Individual Causal Structure Learning from Population Data","authors":[{"name":"Wei Chen","slug":"wei-chen","given":"Wei","family":"Chen","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"},{"name":"Xiaokai Huang","slug":"xiaokai-huang","given":"Xiaokai","family":"Huang","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"},{"name":"Zijian Li","slug":"zijian-li","given":"Zijian","family":"Li","affiliation":"Mohamed bin Zayed University of Artificial Intelligence , Abu Dhabi , United Arab Emirates"},{"name":"Ruichu Cai","slug":"ruichu-cai","given":"Ruichu","family":"Cai","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China; Peng Cheng Laboratory , Shenzhen , China"},{"name":"Zhiyi Huang","slug":"zhiyi-huang","given":"Zhiyi","family":"Huang","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"},{"name":"Zhifeng Hao","slug":"zhifeng-hao","given":"Zhifeng","family":"Hao","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China; College of Mathematics and Computer Science , Shantou University , Shantou , China"}],"abstract":"Learning the causal structure of each individual plays a crucial role in neuroscience, biology, and so on. Existing methods consider data from each individual separately, which may yield inaccurate causal structure estimations in limited samples. To leverage more samples, we consider incorporating data from all individuals as population data. We observe that the variables of all individuals are influenced by the common environment variables they share. These shared environment variables can be modeled as latent variables and serve as a bridge connecting data from different individuals. In particular, we propose an Individual Linear Acyclic Model (ILAM) for each individual from population data, which models the individual's variables as being linearly influenced by their parents, in addition to environment variables and noise terms. Theoretical analysis shows that the model is identifiable when all environment variables are non-Gaussian, or even if some are Gaussian with an adequate diversity in the variance of noises for each individual. We then develop an individual causal structures learning method based on the Share Independence Component Analysis technique. Experimental results on synthetic and real-world data demonstrate the correctness of the method even when the sample size of each individual's data is small.","doi":"10.24963/ijcai.2024/786","url":"https://ijcai.org/proceedings/2024/786","pdf":"https://ijcai.org/proceedings/2024/0786.pdf","markdown":"https://ijcai.org/proceedings/2024/786.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/786","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Uncertainty in AI","track":"Main Track","number":786,"pages":{"first":7109,"last":7117},"keywords":["Uncertainty in AI: UAI: Causality, structural causal models and causal inference"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2024] Ruichu Cai, Yunjin Wu, Xiaokai Huang, Wei Chen, Tom ZJ Fu, and Zhifeng Hao. Granger causal representation learning for groups of time series. Science China Information Sciences, 67(5):152103, 2024.","title":"Granger causal representation learning for groups of time series","authors":"Cai","year":"2024","venue":"Science China Information Sciences","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2021] Wei Chen, Ruichu Cai, Kun Zhang, and Zhifeng Hao. Causal discovery in linear non-gaussian acyclic model with multiple latent confounders. IEEE Transactions on Neural Networks and Learning Systems, 33(7):2816-2827, 2021.","title":"Causal discovery in linear non-gaussian acyclic model with multiple latent confounders","authors":"Wei Chen, Ruichu Cai, Kun Zhang, Zhifeng Hao","year":"2021","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2024] Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, and Kun Zhang. Identification of causal structure with latent variables based on higher order cumu- lants. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 20353-20361, 2024.","title":"Identification of causal structure with latent variables based on higher order cumulants","authors":"Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang","year":"2024","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gates and Molenaar, 2012] Kathleen M Gates and Pe- ter CM Molenaar. Group search algorithm recovers ef- fective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1):310-319, 2012.","title":"Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples","authors":"M Kathleen, Peter Cm Gates, Molenaar","year":"2012","venue":"NeuroImage","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gates et al., 2010] Kathleen M Gates, Peter CM Molenaar, Frank G Hillary, Nilam Ram, and Michael J Rovine. Au- tomatic search for fmri connectivity mapping: An alterna- tive to granger causality testing using formal equivalences among sem path modeling, var, and unified sem. NeuroIm- age, 50(3):1118-1125, 2010.","title":"Automatic search for fmri connectivity mapping: An alternative to granger causality testing using formal equivalences among sem path modeling, var, and unified sem","authors":"Kathleen M Gates, C M Peter, Molenaar, G Frank, Nilam Hillary, Michael J Ram, Rovine","year":"2010","venue":"NeuroImage","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ghassami et al., 2018] AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, and Kun Zhang. Multi-domain causal structure learning in linear systems. Advances in Neural Information Processing Systems, 31, 2018.","title":"Multi-domain causal structure learning in linear systems","authors":"Ghassami","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Glasser et al., 2016] Matthew F Glasser, Stephen M Smith, Daniel S Marcus, Jesper LR Andersson, Edward J Auer- bach, Timothy EJ Behrens, Timothy S Coalson, Michael P Harms, Mark Jenkinson, Steen Moeller, et al. The hu- man connectome project's neuroimaging approach. Nature Neuroscience, 19(9):1175-1187, 2016.","title":"The human connectome project's neuroimaging approach","authors":"Stephen M Matthew F Glasser, Daniel S Smith, Marcus, Edward J Jesper Lr Andersson, Timothy Ej Auerbach, Timothy S Behrens, Michael P Coalson, Mark Harms, Steen Jenkinson, Moeller","year":"2016","venue":"Nature Neuroscience","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Günther et al., 2023] Wiebke Günther, Urmi Ninad, and Jakob Runge. Causal discovery for time series from mul- tiple datasets with latent contexts. In Uncertainty in Arti- ficial Intelligence, pages 766-776. PMLR, 2023.","title":"Causal discovery for time series from multiple datasets with latent contexts","authors":"Wiebke Günther, Urmi Ninad, Jakob Runge","year":"2023","venue":"Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hu et al., 2018] Shoubo Hu, Zhitang Chen, Vahid Par- tovi Nia, Laiwan Chan, and Yanhui Geng. Causal infer- ence and mechanism clustering of a mixture of additive noise models. Advances in Neural Information Processing Systems, 31, 2018.","title":"Causal inference and mechanism clustering of a mixture of additive noise models","authors":"Hu","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2019] Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P Xing, and Clark Glymour. Spe- cific and shared causal relation modeling and mechanism- based clustering. Advances in Neural Information Pro- cessing Systems, 32, 2019.","title":"Specific and shared causal relation modeling and mechanismbased clustering","authors":"Huang","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2020a] Biwei Huang, Kun Zhang, Mingming Gong, and Clark Glymour. Causal discovery from multi- ple data sets with non-identical variable sets. In Proceed- ings of the AAAI conference on artificial intelligence, vol- ume 34, pages 10153-10161, 2020.","title":"Causal discovery from multiple data sets with non-identical variable sets","authors":"Huang","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang et al., 2020b] Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, and Bernhard Schölkopf. Causal discovery from heteroge- neous/nonstationary data. The Journal of Machine Learn- ing Research, 21(1):3482-3534, 2020.","title":"Causal discovery from heterogeneous/nonstationary data","authors":"Huang","year":"2020","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kettenring, 1971] Jon R Kettenring. Canonical analysis of several sets of variables. Biometrika, 58(3):433-451, 1971.","title":"Canonical analysis of several sets of variables","authors":"Jon R Kettenring","year":"1971","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Miller et al., 2016] Karla L Miller, Fidel Alfaro-Almagro, Neal K Bangerter, David L Thomas, Essa Yacoub, Junqian Xu, Andreas J Bartsch, Saad Jbabdi, Stama- tios N Sotiropoulos, Jesper LR Andersson, et al. Mul- timodal population brain imaging in the uk biobank prospective epidemiological study. Nature Neuroscience, 19(11):1523-1536, 2016.","title":"Multimodal population brain imaging in the uk biobank prospective epidemiological study","authors":"Karla L Miller, Fidel Alfaro-Almagro, Neal K Bangerter, David L Thomas, Essa Yacoub, Junqian Xu, Andreas J Bartsch, Saad Jbabdi, Stamatios N Sotiropoulos, Jesper Lr Andersson","year":"2016","venue":"Nature Neuroscience","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pashami et al., 2018] Sepideh Pashami, Anders Holst, Juhee Bae, and Sławomir Nowaczyk. Causal discovery using clusters from observational data. In FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018, 2018.","title":"Causal discovery using clusters from observational data","authors":"Sepideh Pashami, Anders Holst, Juhee Bae, Sławomir Nowaczyk","year":"2018","venue":"FAIM'18 Workshop on CausalML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Perry et al., 2022] Ronan Perry, Julius Von Kügelgen, and Bernhard Schölkopf. Causal discovery in heterogeneous environments under the sparse mechanism shift hypothe- sis. Advances in Neural Information Processing Systems, 35:10904-10917, 2022.","title":"Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis","authors":"Ronan Perry, Julius Von Kügelgen, Bernhard Schölkopf","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ramsey et al., 2010] Joseph D Ramsey, Stephen José Han- son, Catherine Hanson, Yaroslav O Halchenko, Russell A Poldrack, and Clark Glymour. Six problems for causal in- ference from fmri. NeuroImage, 49(2):1545-1558, 2010.","title":"Six problems for causal inference from fmri","authors":"Stephen Joseph D Ramsey, Catherine José Hanson, Hanson, O Yaroslav, Russell A Halchenko, Clark Poldrack, Glymour","year":"2010","venue":"NeuroImage","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Richard et al., 2021] Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, and Aapo Hyvarinen. Shared independent component analysis for multi-subject neuroimaging. Advances in Neural Information Process- ing Systems, 34, 2021.","title":"Shared independent component analysis for multi-subject neuroimaging","authors":"Richard","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sachs et al., 2005] Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523-529, 2005.","title":"Causal protein-signaling networks derived from multiparameter single-cell data","authors":"Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, Garry P Nolan","year":"2005","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Saeed et al., 2020] Basil Saeed, Snigdha Panigrahi, and Caroline Uhler. Causal structure discovery from distribu- tions arising from mixtures of dags. In International Con- ference on Machine Learning, pages 8336-8345.","title":"Causal structure discovery from distribu-tions arising from mixtures of dags","authors":"Saeed","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"PMLR, 2020.","title":null,"authors":"Pmlr","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sanchez-Romero et al., 2019] Ruben Sanchez-Romero, Joseph D Ramsey, Kun Zhang, Madelyn RK Glymour, Biwei Huang, and Clark Glymour. Estimating feedfor- ward and feedback effective connections from fmri time series: Assessments of statistical methods. Network Neuroscience, 3(2):274-306, 2019.","title":"Estimating feedforward and feedback effective connections from fmri time series: Assessments of statistical methods","authors":"Ruben Sanchez-Romero, Joseph D Ramsey, Kun Zhang, Madelyn Rk Glymour, Biwei Huang, Clark Glymour","year":"2019","venue":"Network Neuroscience","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shimizu et al., 2006] Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen. A linear non- gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(Oct):2003-2030, 2006.","title":"A linear nongaussian acyclic model for causal discovery","authors":"Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen","year":"2006","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Shimizu et al., 2011] Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, and Kenneth Bollen. Di- rectlingam: A direct method for learning a linear non- gaussian structural equation model. The Journal of Ma- chine Learning Research, 12:1225-1248, 2011.","title":"Directlingam: A direct method for learning a linear nongaussian structural equation model","authors":"Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, Kenneth Bollen","year":"2011","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Smith et al., 2011] Stephen M Smith, Karla L Miller, Gho- lamreza Salimi-Khorshidi, Matthew Webster, Christian F Beckmann, Thomas E Nichols, Joseph D Ramsey, and Mark W Woolrich. Network modelling methods for fmri. NeuroImage, 54(2):875-891, 2011.","title":"Network modelling methods for fmri","authors":"M Stephen, Karla L Smith, Gholamreza Miller, Matthew Salimi-Khorshidi, Christian F Webster, Thomas E Beckmann, Joseph D Nichols, Mark W Ramsey, Woolrich","year":"2011","venue":"NeuroImage","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Spirtes et al., 2000] Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. Causation, prediction, and search. MIT press, 2000.","title":"Causation, prediction, and search","authors":"Peter Spirtes, Richard Clark N Glymour, David Scheines, Heckerman","year":"2000","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang and Drton, 2023] Y Samuel Wang and Mathias Dr- ton. Causal discovery with unobserved confounding and non-gaussian data. Journal of Machine Learning Re- search, 24(271):1-61, 2023.","title":"Causal discovery with unobserved confounding and non-gaussian data","authors":"Y Samuel, Wang, Mathias Drton","year":"2023","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2017] Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, and Bernhard Schölkopf. Causal discov- ery from nonstationary/heterogeneous data: Skeleton es- timation and orientation determination. In IJCAI: Pro- ceedings of the Conference, volume 2017, page 1347. NIH Public Access, 2017.","title":"Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination","authors":"Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf","year":"2017","venue":"IJCAI: Proceedings of the Conference","doi":null,"url":null,"internal_url":"/proceedings/2017/187"},{"position":29,"raw":"[Zhang et al., 2023] Xinhe Zhang, Lin Han, Chenxuan Lu, Roger S McIntyre, Kayla M Teopiz, Yiyi Wang, Hong Chen, and Bing Cao. Brain structural and functional al- terations in individuals with combined overweight/obesity and mood disorders: A systematic review of neuroimaging studies. Journal of Affective Disorders, 2023.","title":"Brain structural and functional alterations in individuals with combined overweight/obesity and mood disorders: A systematic review of neuroimaging studies","authors":"Xinhe Zhang, Lin Han, Chenxuan Lu, Kayla M Roger S Mcintyre, Yiyi Teopiz, Hong Wang, Bing Chen, Cao","year":"2023","venue":"Journal of Affective Disorders","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}