[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127674-en":3,"doc-seo-127674-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127674,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","IMPROVING OUTCOMES IN MACHINE LEARNING AND DATA-DRIVEN LEARNING SYSTEMS USING STRUCTURAL CAUSAL MODELS - A DISSERTATION","This dissertation investigates how structural causal models and causal inference can improve outcomes in machine learning and data-driven learning systems. It motivates limitations in current AI by linking performance issues to causal assumptions and confounding. The work develops and analyzes causal learning frameworks, including potential outcome models and structural causal model (SCM) approaches, and applies them to treatment effect estimation and causal discovery. It further explores root-cause analysis with time-to-event data and causal feature selection to enhance interpretability and domain adaptation.","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nIMPROVING OUTCOMES IN MACHINE LEARNING AND DATA-DRIVEN LEARNING SYSTEMS USING STRUCTURAL CAUSAL MODELS  \nA DISSERTATION  \nSUBMITTED TO THE GRADUATE FACULTY in partial ful􀀌llment of the requirements for the Degree of  \nDOCTOR OF PHILOSOPHY  \nBy  \nHENRY MADUKA MBOGU  \nNorman, Oklahoma  \n2023  \nIMPROVING OUTCOMES IN MACHINE LEARNING AND DATA-DRIVEN LEARNING SYSTEMS USING STRUCTURAL CAUSAL MODELS  \nA DISSERTATION APPROVED FOR THE  \nGALLOGLY COLLEGE OF ENGINEERING  \nBY THE COMMITTEE CONSISTING OF  \nDr. Charles D. Nicholson, Chair  \nDr. Randa Shehab  \nDr. Naveen Kumar  \nDr. Heather Bedle  \n➞ Copyright by HENRY MADUKA MBOGU 2023 All Rights Reserved.  \nAcknowledgments  \nThe successful completion of this work would not have been possible without the invaluable contributions and support of the members of my doctoral committee. Firstly, I extend my heartfelt gratitude to my doctoral advisor and committee chair, Dr. Charles Nicholson, for not only accepting me under his guidance but also for steadfastly leading me through the completion of my dissertation. Our collaboration has been immensely fruitful, and I grew a lot as a researcher thanks to his guidance. Secondly, I am profoundly grateful to Dr. Randa Shehab for her unwavering support throughout this journey. She has been a great mentor and sponsor, and her belief in my abilities has been instrumental in my progress. Thirdly, I extend my sincere appreciation to Dr. Naveen Kumar for his pivotal role during the early stages of my dissertation and for his continued support all the way through to the completion of my doctoral program. Lastly, I would like to thank Dr. Heather Bedle for her watchful support and genuine interest in my work.  \nI want to express my gratitude to my wife, Liz, for her love and support as I navigated this important stage in my life. I am thankful to my mother, Florence, for her prayers and encouragement, and for being my ultimate cheerleader throughout this journey. To my siblings and the entire extended family, I express my heartfelt thanks for always having my back. Many of you have played important roles, directly and indirectly at various stages, contributing signi􀀌cantly to bring me to this very moment. Your support means the world to me.  \nTable of Contents  \nAcknowledgments iv  \nList Of Tables viii  \nList Of Figures ix  \nAbstract x  \n1 Introduction 1  \n1.1 Motivation .................................. 1  \n1.2 Towards Improving ML and AI Outcomes using Causal Inference ... 5  \n1.2.1 Issues and Limitations of Current AI Systems .......... 5  \n1.2.2 Prospects and Opportunities in Causal Inference for Improving AI Outcomes ............................ 8  \n1.3 Objectives .................................. 13  \n1.4 Organization of Dissertation ........................ 14  \n2 Frameworks for Causal Learning 16  \n2.1 Potential Outcome Model Framework ................... 17  \n2.1.1 Assumptions for Causal Inference ................. 21  \n2.2 Structural Causal Model (SCM) Framework ............... 22  \n2.2.1 De􀀌nitions and Assumptions .................... 25  \n2.2.2 Intervention and Counterfactual Analysis ............. 29  \n2.2.3 Identi􀀌cation, Confounding, and Covariate Selection ....... 33  \n3 Causal Inference Tasks 37  \n3.1 Treatment E􀀋ect Estimation ........................ 37  \n3.1.1 Traditional Methods Addressing Ignorability ........... 38  \n3.1.2 Traditional Methods Relaxing Ignorability ............ 42  \n3.1.3 Advanced Estimation Techniques ................. 43  \n3.2 Causal Discovery .............................. 52  \n3.2.1 Causal Structure Modeling ..................... 52  \n3.2.2 Causal Discovery Principles .................... 54  \n3.2.3 Constraint-Based Methods ..................... 56  \n3.2.4 Score-Based Methods ........................ 58  \n3.2.5 Functional Causal Model Approach ................ 59  \n3.2.6 Hybrid Methods and Other Bayesian Network Learning Approaches 61  \n3.3 Towards Integrating Causal Infe","cbCaid1hC3vTS2lt","https://ap.wps.com/l/cbCaid1hC3vTS2lt","pdf",4791150,1,180,"English","en",105,"# Acknowledgments\n# List Of Tables\n# List Of Figures\n# Abstract\n# 1 Introduction\n## 1.1 Motivation\n## 1.2 Towards Improving ML and AI Outcomes using Causal Inference\n## 1.3 Objectives\n## 1.4 Organization of Dissertation\n# 2 Frameworks for Causal Learning\n## 2.1 Potential Outcome Model Framework\n## 2.2 Structural Causal Model (SCM) Framework\n# 3 Causal Inference Tasks\n## 3.1 Treatment Effect Estimation\n## 3.2 Causal Discovery\n## 3.3 Towards Integrating Causal Inference and Machine Learning\n# 4 Data-Driven Root Cause Analysis via Causal Discovery using TimeTo-Event Data\n## 4.3 Methodology\n## 4.4 Problem Description\n## 4.5 Results and Analysis\n## 4.6 Discussion\n# 5 Causal Feature Selection for Machine Learning Interpretability and Domain Adaptation\n## 5.1 Introduction\n## 5.2 Background\n## 5.3 Methods","[{\"question\":\"What problem does the dissertation address in machine learning outcomes?\",\"answer\":\"It focuses on improving outcomes in machine learning and data-driven learning systems by leveraging structural causal models and causal inference rather than relying solely on traditional AI assumptions.\"},{\"question\":\"Which causal learning frameworks are presented?\",\"answer\":\"The dissertation covers potential outcome model frameworks and structural causal model (SCM) frameworks, including assumptions, intervention and counterfactual analysis, identification, confounding, and covariate selection.\"},{\"question\":\"How are the methods applied to real analytical tasks?\",\"answer\":\"It applies causal ideas to treatment effect estimation and causal discovery, then develops data-driven root-cause analysis using time-to-event data and proposes causal feature selection for interpretability and domain adaptation.\"}]","IMPROVING OUTCOMES IN MACHINE LEARNING AND DATA-DRIVEN LEARNING SYSTEMS USING STRUCTURAL CAUSAL MODELS - A DISSERTATION | PDF",1785940710,454,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"improving-outcomes-in-machine-learning-and-data-driven-learning-systems-using-structural-causal-models-a-dissertation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-outcomes-in-machine-learning-and-data-driven-learning-systems-using-structural-causal-models-a-dissertation/127674/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the dissertation address in machine learning outcomes?","Question",{"text":76,"@type":77},"It focuses on improving outcomes in machine learning and data-driven learning systems by leveraging structural causal models and causal inference rather than relying solely on traditional AI assumptions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which causal learning frameworks are presented?",{"text":81,"@type":77},"The dissertation covers potential outcome model frameworks and structural causal model (SCM) frameworks, including assumptions, intervention and counterfactual analysis, identification, confounding, and covariate selection.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the methods applied to real analytical tasks?",{"text":85,"@type":77},"It applies causal ideas to treatment effect estimation and causal discovery, then develops data-driven root-cause analysis using time-to-event data and proposes causal feature selection for interpretability and domain adaptation.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]