[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124209-en":3,"doc-seo-124209-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124209,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Causality with Machine Learning Using the Lububu Method - Diagnosis of African Swine Fever (ASF)","The paper presents a practical application of the “Lububu method” to build a causal machine learning (CML) model for diagnosing African swine fever (ASF). The method emphasizes contextual understanding of ASF and uses technological tools to improve accuracy by identifying cause-and-effect relationships rather than relying on correlation alone. It constructs an experimental framework that gathers comprehensive ASF knowledge, including causes, symptoms, transmission patterns, and diagnostic procedures. By combining AI innovation with epidemiological expertise, it strengthens causal interpretability and supports data-driven, ethical, and globally applicable veterinary solutions.","Business Ecosystem & Strategy IJBES VOL 7 NO 2 (2025) ISSN: 2687-2293  \nAvailable online [at www.bussecon.com](at www.bussecon.com)  \nJournal homepage: [https://www.bussecon.com/ojs/index.php/ijbes](https://www.bussecon.com/ojs/index.php/ijbes)  \n\n|  |  |  |\n| --- | --- | --- |\n| Causality with machine learning using the Lububu method for the |  |  |\n| diagnosis of African swine fever (ASF)\u003Cbr> Steven Lububu (a) *\u003Cbr>(a) Cape Peninsula University of Technology, District Six, Cape Town, South Africa |  |  |\n| ARTICLE INFO\u003Cbr>Article history:\u003Cbr>Received 12 January 2025\u003Cbr>Received in rev. form 20 March 2025 Accepted 09 April 2025\u003Cbr>Keywords:\u003Cbr>Lububu Method, Data Selection, Quantitative Research Methods\u003Cbr>JEL Classification: OC63, I18, Q16 | A B S T R A C T\u003Cbr>This paper presents the practical application of a novel approach, the so-called \"Lububu method\", to develop a causal machine learning model (CML) for the diagnosis of African swine fever (ASF). The Lububu method was developed to build causal machine learning models by focusing on the contextual understanding of a particular phenomenon and using technological tools to improve accuracy. Its main goal is to identify cause-and-effect relationships that can lead to better outcomes in various fields, including manufacturing, energy production, agriculture, transportation, data management, medicine and computer science. In this study, the Lububu method serves as an experimental framework for the construction of a CML model tailored to ASF diagnosis. This involves gathering comprehensive knowledge about ASF, covering aspects such as the causes, symptoms, transmission patterns and diagnostic procedures. This detailed contextual understanding supports the development of a model that can accurately identify ASF-related factors, ultimately increasing diagnostic effectiveness. By combining AI innovation and epidemiological expertise, this approach redefines ASF diagnostics and paves the way for data-driven, ethical and globally applicable solutions for veterinary medicine.\u003Cbr>© 2025 by the authors. Licensee Bussecon International, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International license (CC BY) ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). |  |\n|  |  |  |\n\nIntroduction  \nThe Lububu method is a causal machine learning (CML) approach that improves ASF diagnosis by combining causal inference techniques with machine learning models. Unlike traditional AI models that rely on correlation-based predictions, the Lububu method identifies true causal factors that influence ASF progression, ensuring a more accurate, interpretable and actionable diagnosis. Existing ASF diagnostic models, including PCR tests and deep learning classifiers, often lack causal inference, leading to bias and poor adaptability in the real world (Aguero et al.,2003; Zhu et al., 2024; Lububu & Twum-Darko, 2024) .  \nThis study bridges the gap between AI and veterinary epidemiology by introducing a causal inference-based diagnostic model that improves feature selection, increases model interpretability, and supports real-time ASF surveillance. By embedding causal detection into ASF diagnostics, the Lububu method creates a scalable, generalizable and ethical AI framework for animal disease modeling and outbreak management.  \nThe Lububu method is a novel approach to building causal machine learning (CML) models that focuses on understanding the context of a phenomenon and applying advanced technological tools to improve accuracy. The Lububu method focuses on identifying causeand-effect relationships in data and aims to create highly accurate and interpretable models, making it valuable for various fields such as medicine, agriculture, manufacturing, energy and transportation. The method’s focus on causal inference sets it apart from other machine learning approaches, whic","cbCainYU57AAFs16","https://ap.wps.com/l/cbCainYU57AAFs16","pdf",1870272,1,23,"English","en",105,"# Introduction\n## Motivation and limitations of correlation-based ASF diagnostics\n## Role of the Lububu method in causal inference and interpretability\n## Related AI/ML approaches and diagnostic constraints","[{\"question\":\"What is the Lububu method used for in this study?\",\"answer\":\"It is used as an experimental framework to build a causal machine learning (CML) model for African swine fever (ASF) diagnosis by focusing on contextual understanding and causal inference.\"},{\"question\":\"How does the Lububu method differ from traditional correlation-based AI models?\",\"answer\":\"It aims to identify true cause-and-effect factors influencing ASF progression, improving accuracy and interpretability compared with models that learn from correlations.\"},{\"question\":\"What information does the study incorporate to develop the ASF diagnosis model?\",\"answer\":\"The model development relies on comprehensive ASF knowledge, including causes, symptoms, transmission patterns, and diagnostic procedures, to support identification of ASF-related factors.\"}]","Causality with Machine Learning Using the Lububu Method - 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