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Cited by 129Bayesian Modeling and Causal InferenceLogic, Reasoning, and KnowledgeData Mining Algorithms and Applications

A Survey on Causal Discovery: Theory and Practice

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Zanga, Alessio, Ozkirimli, Elif, Stella, Fabio·2022·International Journal of Approximate Reasoning
DOI 10.1016/j.ijar.2022.09.004
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  • Expert-elicited SCMs are commonly held as the gold standard.

    “Hereby, we ensure close alignment to the true human reasoning process and ensure the quality of the workflow since expert-elicited SCMs are commonly held as the gold standard (Zanga et al., 2022;Zhang et al., 2023).”

    From STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond (2025) · cites this paper as Zanga et al., 2022

    Section 7.2 describes one study that used a causal graph retrieved from literature as a gold standard. Section 2.7 discusses expert-elicited causal knowledge, but the survey does not say that expert-elicited SCMs are commonly regarded as the gold standard. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 79

  1. H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716-723, 1974.
  2. Juan I Alonso-Barba, Jose A Gámez, Jose M Puerta, et al. Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes. International journal of approximate reasoning, 54(4):429-451, 2013.
  3. Steen A Andersson, David Madigan, and Michael D Perlman. A characterization of markov equivalence classes for acyclic digraphs. The Annals of Statistics, 25(2):505-541, 1997.
  4. Bryan Andrews, Joseph Ramsey, and Gregory F Cooper. Learning high- dimensional directed acyclic graphs with mixed data-types. In The 2019 ACM SIGKDD Workshop on Causal Discovery, pages 4-21. PMLR, 2019.
  5. Justin J. Anker, Erich Kummerfeld, Alexander Rix, Scott J. Burwell, and Matt G. Kushner. Causal network modeling of the determinants of drinking behavior in comorbid alcohol use and anxiety disorder. Alcoholism: Clinical and Experimental Research, 43(1):91-97, 2019.
  6. Peter Bühlmann, Jonas Peters, and Jan Ernest. Cam: Causal additive models, high-dimensional order search and penalized regression. The Annals of Statistics, 42(6):2526-2556, 2014.
  7. Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, and Zhifeng Hao. Causal discovery from discrete data using hidden compact representation. Advances in neural information processing systems, 2018:2666, 2018.
  8. Enrique Castillo, Jose M Gutierrez, and Ali S Hadi. Expert systems and proba- bilistic network models. Springer Science & Business Media, 2012.
  9. David Maxwell Chickering. Optimal structure identification with greedy search. Journal of machine learning research, 3(Nov):507-554, 2002.
  10. Diego Colombo, Marloes H. Maathuis, Markus Kalisch, and Thomas S. Richard- son. Learning high-dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics, 40(1), Feb 2012.
  11. Pierre Comon. Independent component analysis, a new concept? Signal pro- cessing, 36(3):287-314, 1994.
  12. Mathias Drton and Thomas S Richardson. Iterative conditional fitting for gaus- sian ancestral graph models. arXiv preprint arXiv:1207.4118, 2012. · arXiv
  13. Patrick Forré and Joris M Mooij. Constraint-based causal discovery for non- linear structural causal models with cycles and latent confounders. arXiv preprint arXiv:1807.03024, 2018. · arXiv
  14. Dan Geiger and David Heckerman. Learning gaussian networks. In Uncertainty Proceedings 1994, pages 235-243. Elsevier, 1994.
  15. Clark Glymour, Kun Zhang, and Peter Spirtes. Review of causal discovery methods based on graphical models. Frontiers in genetics, 10:524, 2019.
  16. Madelyn Glymour, Judea Pearl, and Nicholas P Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016.
  17. Alain Hauser and Peter Bühlmann. Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs. The Journal of Machine Learning Research, 13(1):2409-2464, 2012.
  18. Jennifer L Hill. Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1):217-240, 2011.
  19. Huining Hu, Zhentao Li, and Adrian R Vetta. Randomized experimental design for causal graph discovery. Advances in neural information processing systems, 27, 2014.
  20. Antti Hyttinen, Frederick Eberhardt, and Matti Järvisalo. Constraint-based causal discovery: Conflict resolution with answer set programming. In UAI, pages 340-349, 2014.
  21. Antti Hyttinen, Paul Saikko, Matti Järvisalo, et al. A core-guided approach to learning optimal causal graphs. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017). International Joint Confer- ences on Artificial Intelligence, 2017.
  22. Guido W Imbens. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and statistics, 86(1):4-29, 2004.
  23. Fattaneh Jabbari, Joseph Ramsey, Peter Spirtes, and Gregory Cooper. Discov- ery of causal models that contain latent variables through bayesian scoring of independence constraints. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 142-157. Springer, 2017.
  24. Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, and Elias Bareinboim. Causal discovery from soft interventions with unknown targets: Characterization and learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 9551-9561.
  25. Dominik Janzing and Bernhard Schölkopf. Causal inference using the algorith- mic markov condition. IEEE Transactions on Information Theory, 56(10):5168- 5194, 2010.
  26. Markus Kalisch, Martin Mächler, Diego Colombo, Marloes H. Maathuis, and Peter Bühlmann. Causal inference using graphical models with the R package pcalg. Journal of Statistical Software, 47(11):1-26, 2012.
  27. Allon M Klein, Linas Mazutis, Ilke Akartuna, Naren Tallapragada, Adrian Veres, Victor Li, Leonid Peshkin, David A Weitz, and Marc W Kirschner. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell, 161(5):1187-1201, 2015.
  28. Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, and Elias Bareinboim. Characterization and learning of causal graphs with latent variables from soft interventions. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32.
  29. Daphne Koller and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.
  30. Gustavo Lacerda, Peter L Spirtes, Joseph Ramsey, and Patrik O Hoyer. Discov- ering cyclic causal models by independent components analysis. arXiv preprint arXiv:1206.3273, 2012. · arXiv
  31. Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Huawen Liu, and Shu Hu. A fast pc algorithm for high dimensional causal discovery with multi-core pcs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5):1483-1495, Sep 2019.
  32. Sanghack Lee, Juan D Correa, and Elias Bareinboim. Generalized transporta- bility: Synthesis of experiments from heterogeneous domains. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
  33. Chun Li and Xiaodan Fan. On nonparametric conditional independence tests for continuous variables. Wiley Interdisciplinary Reviews: Computational Statistics, 12(3):e1489, 2020.
  34. Daniel Marbach, Thomas Schaffter, Claudio Mattiussi, and Dario Floreano. Gen- erating realistic in silico gene networks for performance assessment of reverse engineering methods. Journal of computational biology, 16(2):229-239, 2009.
  35. Florian Markowetz, Steffen Grossmann, and Rainer Spang. Probabilistic soft interventions in conditional gaussian networks. In Robert G. Cowell and Zoubin Ghahramani, editors, Proceedings of the Tenth International Workshop on Arti- ficial Intelligence and Statistics, volume R5 of Proceedings of Machine Learning Research, pages 214-221. PMLR, 06-08 Jan 2005. Reissued by PMLR on 30 March 2021.
  36. Adam Massmann, Pierre Gentine, and Jakob Runge. Causal inference for process understanding in earth sciences. arXiv preprint arXiv:2105.00912, 2021. · arXiv
  37. Christopher Meek. Graphical Models: Selecting causal and statistical models. PhD thesis, PhD thesis, Carnegie Mellon University, 1997.
  38. Christopher Meek. Causal inference and causal explanation with background knowledge. arXiv preprint arXiv:1302.4972, 2013. · arXiv
  39. Joris M Mooij and Tom Claassen. Constraint-based causal discovery using par- tial ancestral graphs in the presence of cycles. In Conference on Uncertainty in Artificial Intelligence, pages 1159-1168. PMLR, 2020.
  40. Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf. Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17(1):1103-1204, 2016.
  41. Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, and Huan Liu. Causal inference for time series analysis: Problems, methods and evaluation. Knowledge and Information Systems, pages 1-45, 2021.
  42. Mario Nagase and Yutaka Kano. Identifiability of nonrecursive structural equa- tion models. Statistics & Probability Letters, 122:109-117, 2017.
  43. Ana Rita Nogueira, João Gama, and Carlos Abreu Ferreira. Causal discovery in machine learning: Theories and applications. Journal of Dynamics & Games, 8(3):203, 2021.
  44. Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, and João Gama. Methods and tools for causal discovery and causal inference. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, page e1449, 2022.
  45. Juan Miguel Ogarrio, Peter Spirtes, and Joe Ramsey. A hybrid causal search algorithm for latent variable models. In Conference on Probabilistic Graphical Models, pages 368-379. PMLR, 2016.
  46. Judea Pearl. Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016, 2018. · arXiv
  47. Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, Inc., USA, 1st edition, 2018.
  48. Jonas Peters and Peter Bühlmann. Structural intervention distance for evaluat- ing causal graphs. Neural computation, 27(3):771-799, 2015.
  49. Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.
  50. Joseph Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, and Clark Gly- mour. A million variables and more: the fast greedy equivalence search algo- rithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. International journal of data science and analytics, 3(2):121-129, 2017.
  51. Joseph D Ramsey, Kun Zhang, Madelyn Glymour, Ruben Sanchez Romero, Biwei Huang, Imme Ebert-Uphoff, Savini Samarasinghe, Elizabeth A Barnes, and Clark Glymour. Tetrad-a toolbox for causal discovery. In 8th International Workshop on Climate Informatics, 2018.
  52. Kari Rantanen, Antti Hyttinen, and Matti Järvisalo. Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach. In- ternational Journal of Approximate Reasoning, 117:29-49, 2020.
  53. Kari Rantanen, Antti Hyttinen, and Matti Järvisalo. Learning optimal cyclic causal graphs from interventional data. In International Conference on Proba- bilistic Graphical Models, pages 365-376. PMLR, 2020.
  54. Thomas Richardson and Peter Spirtes. Ancestral graph markov models. The Annals of Statistics, 30(4):962-1030, 2002.
  55. Thomas S Richardson. A discovery algorithm for directed cyclic graphs. arXiv preprint arXiv:1302.3599, 2013. · arXiv
  56. Jorma Rissanen. Modeling by shortest data description. Automatica, 14(5):465- 471, 1978.
  57. Dominik Rothenhäusler, Christina Heinze, Jonas Peters, and Nicolai Mein- shausen. Backshift: Learning causal cyclic graphs from unknown shift inter- ventions. arXiv preprint arXiv:1506.02494, 2015. · arXiv
  58. 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.
  59. Richard Scheines and Joseph Ramsey. Measurement error and causal discovery. In CEUR workshop proceedings, volume 1792, page 1. NIH Public Access, 2016.
  60. Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. Toward causal representation learning. Proceedings of the IEEE, 109(5):612-634, 2021.
  61. Gideon Schwarz. Estimating the dimension of a model. The annals of statistics, pages 461-464, 1978.
  62. Marco Scutari. Learning bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3):1-22, 2010.
  63. Marco Scutari. An empirical-bayes score for discrete bayesian networks. In Conference on probabilistic graphical models, pages 438-448. PMLR, 2016.
  64. Marco Scutari. Bayesian network constraint-based structure learning algorithms: Parallel and optimized implementations in the bnlearn R package. Journal of Statistical Software, 77(2):1-20, 2017.
  65. Amirhossein Shahbazinia, Saber Salehkaleybar, and Matin Hashemi. Par- alingam: Parallel causal structure learning for linear non-gaussian acyclic mod- els. arXiv preprint arXiv:2109.13993, 2021. · arXiv
  66. Xinpeng Shen, Sisi Ma, Prashanthi Vemuri, and Gyorgy Simon. Challenges and opportunities with causal discovery algorithms: application to alzheimer's pathophysiology. Scientific reports, 10(1):1-12, 2020.
  67. Shohei Shimizu. Lingam: Non-gaussian methods for estimating causal struc- tures. Behaviormetrika, 41(1):65-98, 2014.
  68. Shohei Shimizu and Patrick Blöbaum. Recent Advances in Semi-Parametric Methods for Causal Discovery, chapter 5, pages 111-130. 2020.
  69. Ilya Shpitser and Judea Pearl. Complete identification methods for the causal hierarchy. Journal of Machine Learning Research, 9(9), 2008.
  70. Peter Spirtes. An anytime algorithm for causal inference. In International Work- shop on Artificial Intelligence and Statistics, pages 278-285. PMLR, 2001.
  71. Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. Cau- sation, prediction, and search. MIT press, 2000.
  72. Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M Mooij, and Bernhard Schölkopf. Probabilistic latent variable models for distinguishing between cause and effect. Advances in neural information processing systems, 23:1687-1695, 2010.
  73. Natasa Tagasovska, Valérie Chavez-Demoulin, and Thibault Vatter. Distinguish- ing cause from effect using quantiles: Bivariate quantile causal discovery. In International Conference on Machine Learning, pages 9311-9323. PMLR, 2020.
  74. Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, and Ioannis Tsamardi- nos. Constraint-based causal discovery with mixed data. International journal of data science and analytics, 6(1):19-30, 2018.
  75. Ioannis Tsamardinos, Constantin F Aliferis, Alexander R Statnikov, and Er Stat- nikov. Algorithms for large scale markov blanket discovery. In FLAIRS confer- ence, volume 2, pages 376-380. St. Augustine, FL, 2003.
  76. Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis. The max-min hill-climbing bayesian network structure learning algorithm. Machine learning, 65(1):31-78, 2006.
  77. Tim Van den Bulcke, Koenraad Van Leemput, Bart Naudts, Piet van Remortel, Hongwu Ma, Alain Verschoren, Bart De Moor, and Kathleen Marchal. Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC bioinformatics, 7(1):1-12, 2006.
  78. Karren Yang, Abigail Katcoff, and Caroline Uhler. Characterizing and learning equivalence classes of causal dags under interventions. In International Confer- ence on Machine Learning, pages 5541-5550. PMLR, 2018.
  79. Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P Xing. Dags with no tears: Continuous optimization for structure learning. arXiv preprint arXiv:1803.01422, 2018. · arXiv

Cited by

Sorted by citations

Authors
Zanga, A., Ozkirimli, E., & Stella, F.
Affiliations
University of Milano-Bicocca (IT)
Corresponding author
Zanga, Alessio
ORCID
Venue
International Journal of Approximate Reasoning
Publisher
Elsevier BV
Published
2022-12
Volume
151
Pages
101-129
ISSN
0888-613X (ISSN-L)
DOI
10.1016/j.ijar.2022.09.004
OpenAlex
W4295346149
Cited by
129
References
6
Citations by year
2026: 39 · 2025: 44 · 2024: 35 · 2023: 11
FWCI
7.02
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Approximate Reasoning (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • BOA (University of Milano-Bicocca) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • F Hoffmann-La Roche AG
  • F. Hoffmann-La Roche
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Keywords
causal discovery, causal inference, causal graphs, causal effect estimation, causal discovery algorithms

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