[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117110-en":3,"doc-seo-117110-105":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},117110,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Causal and Trustworthy Machine Learning: Methods and Applications - Doctor of Philosophy Thesis","This thesis studies the intersection between machine learning and causal inference, showing how both areas can improve each other. It develops and analyzes methods that use machine learning to compute causal quantities, and uses causal inference ideas to enable invariant predictions across unseen treatment regimes. The work also investigates trustworthy machine learning through interpretability and fairness using a causal lens, while explicitly addressing the assumptions required for causal identification and relaxing them when feasible. It covers seven chapters, from causal effect estimation under partial graph knowledge to fair learning.","Causal and Trustworthy Machine Learning: Methods and Applications  \nLimor Gultchin  \nKeble College University of Oxford  \nA thesis submitted for the degree of Doctor of Philosophy  \nMichaelmas 2023  \nAcknowledgements  \nI would like to thank my co-advisors, Prof. Matt Kusner and Prof. Varun Kanade for their wise guidance and kindness through these past few years, as well as to Prof. Ricardo Silva who has become an adopting advisor to whom I owe much of my learning through this journey. I would also like to thank the Alan Turing Institute for the kind support of this work and my studies.  \nI have learned and benefited from all collaborators with whom I have had the honor of working: Dr. David Watson, Caroline Yuchen Zhu, Dr. Ankur Taly, Dr. Vincent Cohen-Addad, Dr. Sophie Giffard-Roisin, Dr. Frederik Mallmann-Trenn, Siyuan Guo, Dr. Alan Malek and Dr. Silvia Chiappa. I am also grateful for the intellectual exchanges facilitated by the UCL causal inference group and the many enriching discussions it fostered.  \nEarlier in my research path, I am grateful to have had the support and encouragement of mentors and advisors without whom I would not have made it thus far: Dr. Adam Kalai, Dr. Ofra Amir, Prof. Barbara Grosz, Prof. Stuart Shieber, and Dr. Scott Hale.  \nI could not have completed this work without the warm and dedicated support of family and friends, especially my parents, Josef and Etia, and my partner, Raphael Köster, who has put up with the sacrifice of many an evening and a weekend for the completion of this work.  \nAbstract  \nThis work focuses on the intersection of machine learning and causal inference and the way in which the two fields can enhance each other by sharing ideas: utilizing machine learning techniques for the computation of causal quantities, the use of ideas from causal inference for invariant predictions under unseen treatment regimes, and the exploration of topics in trustworthy machine learning, including interpretability and fairness, with a causal lens. In each one of the presented works, we grappled with the strength of assumptions needed to utilize causal inference techniques and relax portions of them when possible.  \nIn Chapter 1, we introduce the motivation behind the works and the challenges that sparked this plan of study. Chapter 2 provides a foundation on basic topics in causal machine learning and trustworthy machine learning. In Chapter 3, we introduce a causal effect estimation method under partial causal graph knowledge. In Chapter 4, we look at causal effect estimation in complex data settings, such as images, text, and gene expression networks, and propose an invariant estimation approach utilizing crude interventions. In Chapter 5, we provide a causal perspective on explainable machine learning, unifying existing works and providing a sound and complete algorithm involving the concepts of sufficiency and necessity. Finally, in Chapters 6 and 7, we introduce methods and investigations in fair machine learning.  \nContents  \n1 Introduction 1  \n1.1 The Importance of Cause and Effect .................. 1  \n1.2 Machine Learning Failure Modes .................... 3  \n1.3 Machine Learning for Causality, Causality for Machine Learning ... 6  \n1.4 Contributions ............................... 7  \n2 Background 10  \n2.1 Basic Toolkit ............................... 10  \n2.1.1 Independence: Marginal and Conditional ............ 10  \n2.1.2 The i.i.d. Assumption in Machine Learning and Causal Inference 11  \n2.2 Causal Inference for Machine Learning ................. 12  \n2.2.1 Different Approaches to Causality ................ 12  \n2.2.2 Structural Causal Models .................... 14  \n2.2.3 d-separation and Formalization of Dependencies ........ 16  \n2.2.4 Causal Effects: ATE, CATE, ITE ................ 17  \n2.2.5 The do-calculus .......................... 19  \n2.2.6 Canonical Identification Strategies ................ 20  \n2.2.7 Pearl’s Causal Hierarchy ..................... 23  \n2.2.8 Estimati","cbCaiuGuBcfg2Iw0","https://ap.wps.com/l/cbCaiuGuBcfg2Iw0","pdf",6998121,1,201,"English","en",105,"# Acknowledgements\n# Abstract\n# Contents\n## Introduction\n## Background\n## Differentiable Causal Backdoor Discovery\n## Operationalizing Complex Causes: A Pragmatic View of Mediation","[{\"question\":\"What is the main focus of this thesis?\",\"answer\":\"The thesis focuses on combining machine learning with causal inference to compute causal quantities, enable invariant predictions, and advance trustworthy machine learning through interpretability and fairness from a causal perspective.\"},{\"question\":\"How does the thesis connect machine learning and causal inference?\",\"answer\":\"It uses machine learning techniques to estimate causal quantities while applying causal inference concepts to support predictions that remain consistent under unseen treatment regimes.\"},{\"question\":\"Which trustworthy machine learning topics are addressed?\",\"answer\":\"The thesis covers interpretability and algorithmic fairness, including explainable machine learning and fair prediction or policy optimization, grounded in causal reasoning.\"}]","Causal and Trustworthy Machine Learning: Methods and Applications - 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