[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117561-en":3,"doc-seo-117561-105":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},117561,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Essays on Machine Learning in Causal Inference and Prediction","Research bridges machine learning and econometrics by tackling both predictive modeling and causal inference challenges. The work integrates machine learning’s strengths in pattern recognition with econometrics’ causal identification framework to improve model interpretability and effectiveness for economics and social sciences. Emphasis is placed on adapting machine learning techniques for causal inference to support more robust predictions and better policy decisions. The dissertation studies deep learning with endogeneity handling, compares prediction-focused models and combinations, and develops ML-based tools for partial derivatives and treatment-effect estimation under high-dimensional settings.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nEssays on Machine Learning in Causal Inference and Prediction  \nPermalink  \n[https://escholarship.org/uc/item/7104030g](https://escholarship.org/uc/item/7104030g)  \nISBN  \n9798263309015  \nAuthor  \nDing, Yifei  \nPublication Date  \n2025-08-27  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nEssays on Machine Learning in Causal Inference and Prediction  \nA Dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEconomics  \nby  \nYifei Ding  \nSeptember 2025  \nDissertation Committee:  \nDr. Ruoyao Shi, Chairperson  \nDr. Tae-Hwy Lee  \nDr. Weixin Yao  \nCopyright by Yifei Ding 2025  \nThe Dissertation of Yifei Ding is approved:  \n\n|  |\n| --- |\n|  |\n\nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nThe completion of dissertation means my Ph.D. journey finally comes to the final end. Before everything else, I would like to express my heartfelt gratitude to my advisor, Professor Ruoyao Shi, for her invaluable guidance, persistent support, and genuine encouragement throughout my Ph.D. journey. Her professional integrity and kindness have served as a role model for my future profession. I am also truly grateful to my co-advisor, Professor Aman Ullah, whose profound wisdom and insightful mentorship laid the foundation for my early academic career. His guidance helped me find my research direction and cultivate the intellectual taste that shaped this dissertation, and I wish him the very best in health and life. I further extend my sincere thanks to Professor Tae-Hwy Lee for his continuous support, encouragement, and constructive feedback, which have been instrumental to my research and academic growth.  \nI would also like to acknowledge Professor Weixin Yao, Professor Jiang-ting Guo, Professor Siyang Xiong and Professor Yang Xie for their valuable feedback and timely advice during important stages of my Ph.D., which guided me through several critical decisions. My heartfelt thanks also go to Gary Kuzas for his dedicated help and constant assistance, which became an essential part of successfully completing this journey.  \nMost of all, I am indebted to my family—my grandmother, parents, and brother—for their unconditional support and sacrifices. Without them, I could not have reached this milestone. In particular, my grandmother’s love and care during my childhood, especially her dedication to my education, were essential in shaping who I am today.  \nI am also truly grateful to Brenda Harris, Ryan Hodge, Scot Myhr, Sherly Myhr, Kevin Brennfleck, Kay Marie Brennfleck, Amy Zheng, Alan Claassen Thrush, Beth Claassen Thrush, Yuqing Fu and many others from Crest Community Church, whose kindness and fellowship enriched my life and made my journey more meaningful.  \nLastly, my journey has been made richer by the friendship and kindness of so many peers and friends, including Dawei Jian, Chuan Zhang, Daanish Padha, Zhuozhen Zhao, Dayang Li, Che Li, Yong Ju Lee, Jingyan Guo, Yaojue Xu, Jiahui Liu, Keren Fang, and Mengmeng Dong. To all those who have supported me—whether named here or not—I extend my deepest gratitude.  \nTo my family for their unwavering love To the toil and striving that brought me here And to Him in whom the journey both begins and ends.  \nvi  \nABSTRACT OF THE DISSERTATION  \nEssays on Machine Learning in Causal Inference and Prediction  \nby  \nYifei Ding  \nDoctor of Philosophy, Graduate Program in Economics University of California, Riverside, September 2025  \nDr. Ruoyao Shi, Chairperson  \nMy research bridges the fields of machine learning (ML) and econometrics, addressing both predictive and causal inference challenges. The growing intersection of these two fields is transforming how we approach data analysis in economics and social sciences","cbCaiaeoQ6VqNo6J","https://ap.wps.com/l/cbCaiaeoQ6VqNo6J","pdf",2012170,1,228,"English","en",105,"# Abstract\n# Chapter 1 - Overview of the dissertation\n# Chapter 2 - Deep learning for heterogeneity and endogeneity\n# Chapter 3 - Comparative analysis and prediction combination\n# Chapter 4 - Estimating partial derivatives for causal understanding\n# Chapter 5 - Double machine learning and balancing for continuous treatments","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To bridge machine learning and econometrics by integrating predictive modeling with causal inference so that models become more interpretable and useful for economics and social sciences.\"},{\"question\":\"How does Chapter 2 address endogeneity and individual heterogeneity?\",\"answer\":\"It presents an architecture and theoretical foundations using deep learning, including simulation results and the use of generated regressors to address endogeneity while handling individual heterogeneity.\"},{\"question\":\"Which methods are compared for continuous treatment effects in Chapter 5?\",\"answer\":\"Chapter 5 compares double machine learning with balancing approaches for estimating effects of continuous treatments, using semi-synthetic data based on Snapchat user data to evaluate scalability, flexibility, and precision.\"}]","Essays on Machine Learning in Causal Inference and Prediction | 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is the main goal of the dissertation?","Question",{"text":75,"@type":76},"To bridge machine learning and econometrics by integrating predictive modeling with causal inference so that models become more interpretable and useful for economics and social sciences.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Chapter 2 address endogeneity and individual heterogeneity?",{"text":80,"@type":76},"It presents an architecture and theoretical foundations using deep learning, including simulation results and the use of generated regressors to address endogeneity while handling individual heterogeneity.",{"name":82,"@type":73,"acceptedAnswer":83},"Which methods are compared for continuous treatment effects in Chapter 5?",{"text":84,"@type":76},"Chapter 5 compares double machine learning with balancing approaches for estimating effects of continuous treatments, using semi-synthetic data based on Snapchat user data to evaluate scalability, flexibility, and 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