{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:59:14Z","timestamp":1781225954879,"version":"3.54.1"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"CNPq","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004901","name":"FAPEMIG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116005","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:17:52Z","timestamp":1776417472000},"page":"116005","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Causal-Nest: A framework for automated causal discovery and inference"],"prefix":"10.1016","volume":"343","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0282-0689","authenticated-orcid":false,"given":"Gustavo F.V.","family":"de Oliveira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0713-0583","authenticated-orcid":false,"given":"Fabr\u00edcio A.","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9893-0876","authenticated-orcid":false,"given":"Marcus H.S.","family":"Mendes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116005_b1","series-title":"Causality","author":"Pearl","year":"2009"},{"issue":"6464","key":"10.1016\/j.knosys.2026.116005_b2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.knosys.2026.116005_b3","series-title":"Conference on Fairness, Accountability and Transparency","first-page":"160","article-title":"Runaway feedback loops in predictive policing","author":"Ensign","year":"2018"},{"key":"10.1016\/j.knosys.2026.116005_b4","series-title":"Probabilistic and Causal Inference: The Works of Judea Pearl","first-page":"765","article-title":"Causality for machine learning","author":"Sch\u00f6lkopf","year":"2022"},{"issue":"5","key":"10.1016\/j.knosys.2026.116005_b5","doi-asserted-by":"crossref","DOI":"10.1145\/3409382","article-title":"Causality-based feature selection: Methods and evaluations","volume":"53","author":"Yu","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2026.116005_b6","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4845","article-title":"Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects","author":"Alcorn","year":"2019"},{"key":"10.1016\/j.knosys.2026.116005_b7","series-title":"Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3","first-page":"30","article-title":"Dynamic causality knowledge graph generation for supporting the chatbot healthcare system","author":"Yu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116005_b8","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1007\/s10115-021-01621-0","article-title":"Causal inference for time series analysis: Problems, methods and evaluation","volume":"63","author":"Moraffah","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.knosys.2026.116005_b9","series-title":"Learning Bayesian Network Model Structure from Data","author":"Margaritis","year":"2003"},{"key":"10.1016\/j.knosys.2026.116005_b10","series-title":"FLAIRS","first-page":"376","article-title":"Algorithms for large scale Markov blanket discovery","volume":"Vol. 2","author":"Tsamardinos","year":"2003"},{"issue":"3","key":"10.1016\/j.knosys.2026.116005_b11","article-title":"Estimating high-dimensional directed acyclic graphs with the PC-algorithm","volume":"8","author":"Kalisch","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116005_b12","series-title":"Causation, Prediction, and Search","author":"Spirtes","year":"2000"},{"key":"10.1016\/j.knosys.2026.116005_b13","series-title":"2023 IEEE International Conference on Data Mining","first-page":"668","article-title":"Causal discovery by continuous optimization with conditional independence constraint: Methodology and performance","author":"Xia","year":"2023"},{"issue":"Supplement_2","key":"10.1016\/j.knosys.2026.116005_b14","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae411","article-title":"A hybrid constrained continuous optimization approach for optimal causal discovery from biological data","volume":"40","author":"Zhu","year":"2024","journal-title":"Bioinformatics"},{"issue":"6","key":"10.1016\/j.knosys.2026.116005_b15","doi-asserted-by":"crossref","first-page":"3472","DOI":"10.1109\/TKDE.2025.3546607","article-title":"Learning causal representations based on a GAE embedded autoencoder","volume":"37","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116005_b16","series-title":"Introduction to Linear Regression Analysis","author":"Montgomery","year":"2012"},{"key":"10.1016\/j.knosys.2026.116005_b17","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Friedman","year":"2001"},{"key":"10.1016\/j.knosys.2026.116005_b18","series-title":"Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction","author":"Imbens","year":"2015"},{"key":"10.1016\/j.knosys.2026.116005_b19","series-title":"Causal-learn: Causal discovery in Python","author":"Zheng","year":"2023"},{"issue":"25","key":"10.1016\/j.knosys.2026.116005_b20","first-page":"1","article-title":"pgmpy: A Python Toolkit for Bayesian Networks","author":"Ankan","year":"2024","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"10.1016\/j.knosys.2026.116005_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v035.i03","article-title":"Learning Bayesian Networks with the bnlearn R package","volume":"35","author":"Scutari","year":"2010","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.knosys.2026.116005_b22","series-title":"Proceedings of the 2023 Causal Analysis Workshop Series","first-page":"40","article-title":"Py-Tetrad and RPy-Tetrad: A new Python interface with R support for tetrad causal search","volume":"Vol. 223","author":"Ramsey","year":"2023"},{"key":"10.1016\/j.knosys.2026.116005_b23","series-title":"Proceedings of the 27th ACM SIGKDD","first-page":"4072","article-title":"Causal inference and machine learning in practice with EconML and CausalML: Industrial use cases at Microsoft, TripAdvisor, Uber","author":"Syrgkanis","year":"2021"},{"key":"10.1016\/j.knosys.2026.116005_b24","series-title":"CausalML: Python package for causal machine learning","author":"Chen","year":"2020"},{"key":"10.1016\/j.knosys.2026.116005_b25","series-title":"DoWhy: An End-to-End library for causal inference","author":"Sharma","year":"2020"},{"issue":"11","key":"10.1016\/j.knosys.2026.116005_b26","doi-asserted-by":"crossref","first-page":"eaau4996","DOI":"10.1126\/sciadv.aau4996","article-title":"Detecting and quantifying causal associations in large nonlinear time series datasets","volume":"5","author":"Runge","year":"2019","journal-title":"Sci. Adv."},{"key":"10.1016\/j.knosys.2026.116005_b27","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202400181","article-title":"CAnDOIT: Causal discovery with observational and interventional data from Time-Series","author":"Castri","year":"2024","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.knosys.2026.116005_b28","series-title":"Causal discovery toolbox: Uncover causal relationships in Python","author":"Kalainathan","year":"2019"},{"key":"10.1016\/j.knosys.2026.116005_b29","series-title":"gCastle: A Python toolbox for causal discovery","author":"Zhang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116005_b30","series-title":"CausalNex","author":"QuantumBlack Labs","year":"2021"},{"key":"10.1016\/j.knosys.2026.116005_b31","series-title":"Salesforce CausalAI Library: A fast and scalable framework for causal analysis of time series and tabular data","author":"Arpit","year":"2023"},{"key":"10.1016\/j.knosys.2026.116005_b32","series-title":"Exploring Network Structure, Dynamics, and Function Using Networkx","author":"Hagberg","year":"2008"},{"issue":"3","key":"10.1016\/j.knosys.2026.116005_b33","first-page":"613","article-title":"Tests for departure from normality. Empirical results for the distributions of b2 and b1","volume":"60","author":"D\u2019agostino","year":"1973","journal-title":"Biometrika"},{"issue":"3","key":"10.1016\/j.knosys.2026.116005_b34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v035.i03","article-title":"Learning Bayesian networks with the bnlearn R package","volume":"35","author":"Scutari","year":"2010","journal-title":"J. Stat. Softw."},{"issue":"6","key":"10.1016\/j.knosys.2026.116005_b35","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1214\/14-AOS1260","article-title":"CAM: Causal additive models, high-dimensional order search and penalized regression","volume":"42","author":"B\u00fchlmann","year":"2014","journal-title":"Ann. Statist."},{"issue":"1","key":"10.1016\/j.knosys.2026.116005_b36","first-page":"2273","article-title":"Concave penalized estimation of sparse Gaussian Bayesian networks","volume":"16","author":"Aragam","year":"2015","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"10.1016\/j.knosys.2026.116005_b37","doi-asserted-by":"crossref","DOI":"10.18637\/jss.v091.i11","article-title":"Learning Large-Scale Bayesian networks with the sparsebn package","volume":"91","author":"Aragam","year":"2019","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.knosys.2026.116005_b38","first-page":"39","article-title":"Learning functional causal models with generative neural networks","author":"Goudet","year":"2018","journal-title":"Explain. Interpret. Model. Comput. Vis. Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116005_b39","series-title":"Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems","first-page":"929","article-title":"PyTorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation","volume":"Vol. 2","author":"Ansel","year":"2024"},{"issue":"6A","key":"10.1016\/j.knosys.2026.116005_b40","doi-asserted-by":"crossref","first-page":"3151","DOI":"10.1214\/17-AOS1654","article-title":"High-dimensional consistency in score-based and hybrid structure learning","volume":"46","author":"Nandy","year":"2018","journal-title":"Ann. Statist."},{"issue":"11","key":"10.1016\/j.knosys.2026.116005_b41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v047.i11","article-title":"Causal inference using graphical models with the R Package pcalg","volume":"47","author":"Kalisch","year":"2012","journal-title":"J. Stat. Softw."},{"issue":"10","key":"10.1016\/j.knosys.2026.116005_b42","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu","year":"2006","journal-title":"J. Mach. Learn. Res."},{"issue":"219","key":"10.1016\/j.knosys.2026.116005_b43","first-page":"1","article-title":"Structural agnostic modeling: Adversarial learning of causal graphs","volume":"23","author":"Kalainathan","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116005_b44","series-title":"Uncertainty in Artificial Intelligence","first-page":"1052","article-title":"Greedy relaxations of the sparsest permutation algorithm","author":"Lam","year":"2022"},{"key":"10.1016\/j.knosys.2026.116005_b45","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1613\/jair.4039","article-title":"Learning optimal Bayesian networks: A shortest path perspective","volume":"48","author":"Yuan","year":"2013","journal-title":"J. Artificial Intelligence Res."},{"key":"10.1016\/j.knosys.2026.116005_b46","series-title":"Machine Learning and Knowledge Discovery in Databases","first-page":"451","article-title":"Area under the Precision-Recall Curve: Point estimates and confidence intervals","author":"Boyd","year":"2013"},{"key":"10.1016\/j.knosys.2026.116005_b47","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","article-title":"The max-min hill-climbing Bayesian network structure learning algorithm","volume":"65","author":"Tsamardinos","year":"2006","journal-title":"Mach. Learn."},{"issue":"3","key":"10.1016\/j.knosys.2026.116005_b48","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1162\/NECO_a_00708","article-title":"Structural intervention distance for evaluating causal graphs","volume":"27","author":"Peters","year":"2015","journal-title":"Neural Comput."},{"issue":"5721","key":"10.1016\/j.knosys.2026.116005_b49","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1126\/science.1105809","article-title":"Causal Protein-Signaling networks derived from Multiparameter Single-Cell data","volume":"308","author":"Sachs","year":"2005","journal-title":"Science"},{"issue":"60","key":"10.1016\/j.knosys.2026.116005_b50","first-page":"1","article-title":"Causal-learn: Causal discovery in python","volume":"25","author":"Zheng","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116005_b51","series-title":"Airfoil Self-Noise","author":"Brooks","year":"2014"},{"key":"10.1016\/j.knosys.2026.116005_b52","series-title":"Algerian Forest Fires","author":"Abid","year":"2019"},{"key":"10.1016\/j.knosys.2026.116005_b53","series-title":"Yacht Hydrodynamics","author":"Gerritsma","year":"2013"},{"issue":"4","key":"10.1016\/j.knosys.2026.116005_b54","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.dss.2009.05.016","article-title":"Modeling wine preferences by data mining from physicochemical properties","volume":"47","author":"Cortez","year":"2009","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.knosys.2026.116005_b55","series-title":"Auto MPG","author":"Quinlan","year":"1993"},{"key":"10.1016\/j.knosys.2026.116005_b56","series-title":"Replication Data for: Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion","author":"Fuller","year":"2020"},{"key":"10.1016\/j.knosys.2026.116005_b57","series-title":"Abalone","author":"Nash","year":"1995"},{"key":"10.1016\/j.knosys.2026.116005_b58","series-title":"Iranian Churn","year":"2020"},{"issue":"1","key":"10.1016\/j.knosys.2026.116005_b59","doi-asserted-by":"crossref","DOI":"10.9734\/ajrcos\/2025\/v18i1548","article-title":"Predicting customer churn in telecommunications with machine learning models","volume":"18","author":"Hossain","year":"2025","journal-title":"Asian J. Res. Comput. Sci."},{"key":"10.1016\/j.knosys.2026.116005_b60","unstructured":"L. Castri, S. Mghames, M. Hanheide, N. Bellotto, Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios, in: Proceedings of the Conference on Causal Learning and Reasoning (CLeaR), 2023."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007318?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007318?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:14:52Z","timestamp":1781223292000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126007318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":60,"alternative-id":["S0950705126007318"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116005","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Causal-Nest: A framework for automated causal discovery and inference","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116005"}}