[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121492-en":3,"doc-seo-121492-105":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},121492,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","The Legislative State and the Republican Revolution: A Causal Machine Learning Approach - Dissertation","This dissertation examines how the 1994 Republican Revolution affected congressional oversight capacity through a combined toolkit of traditional causal inference and causal machine learning. The analysis uses a new dataset covering the full set of publicly available Government Accountability Office (GAO) materials, treating GAO as Congress’s understudied “watchdog” oversight and auditing agency. Findings show harmful effects on GAO oversight capacity, with Chapter 1 estimating multiple dependent-variable impacts, Chapter 2 modeling heterogeneous effects, and Chapter 3 assessing broader causal machine learning applicability in social science debates.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nThe Legislative State and the Republican Revolution: A Causal Machine Learning Approach  \nPermalink  \n[https://escholarship.org/uc/item/1ps7d30j](https://escholarship.org/uc/item/1ps7d30j)  \nISBN  \n9798297647909  \nAuthor  \nRametta, Jack  \nPublication Date  \n2025-09-21  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nThe Legislative State and the Republican Revolution: A Causal Machine Learning Approach  \nBy  \nJACK T. RAMETTA  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nPolitical Science  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \nChristopher Hare, Chair  \nRyan H¨ubert  \n\n| Erik J. Engstrom |\n| --- |\n| Scott MacKenzie |\n\nLauren Peritz Committee in Charge 2025  \n© Jack T. Rametta, 2025 . All rights reserved.  \nTo Sarah  \nii  \nAcknowledgments  \nFirst and foremost I have to thank my partner Sarah for her support throughout graduate school. Without her support I surely would have failed. Thanks as well to my parents and sister for their support along the way.  \nI of course need to give special thanks to my advisors Chris Hare and Ryan H¨ubert for shepherding me through my graduate work. I learned an enormous amount from you both, and my perspective on politics has been shaped by your courses and feedback. I owe you both a great debt for any current or future success. My thanks to my other committee members as well: Erik Engstrom, Scott MacKenzie, and Lauren Peritz, for their guidance and feedback.  \nI also must thank Sam Fuller for his support and collaboration over the years. Last, and undoubtedly most important, I must acknowledge the support of my dogs Jaco and Stella, who, in their own way, supported (or perhaps hindered) my graduate work.  \nDissertation Chair: Christopher D. Hare Jack T. Rametta (ID: 917850880) The Legislative State and the Republican Revolution: A Causal Machine Learning  \nApproach  \nAbstract  \nIn this dissertation, I interrogate the effects of the Republican Revolution of 1994 on Congressional oversight capacity using a combination of traditional causal inference methods and causal machine learning approaches. The venue for this investigation is a novel dataset that comprises the universe of published and publicly available material from the Government Accountability Office (GAO), Congress’ understudied “watchdog” oversight and auditing agency. Through my investigation I reveal the deleterious effects of the Republican Revolution on oversight capacity at GAO. In the first chapter, I explore the effects of the Republican Revolution in terms of several different dependent variables of interest. In the second chapter, I explore heterogeneity in these main effects using a novel causal machine learning estimation procedure. Finally, in the third chapter, I explore the broader applicability of causal machine learning methods in the social sciences in light of recent debates. My results contribute to a growing literature that is skeptical of Congress’ ability to effectively oversee the executive branch. This dissertation also contributes to a growing literature that applies blackbox predictive algorithms for inference.  \nContents  \nIntroduction 1  \nBibliography 3  \nChapter 1 . Did the Republican Revolution Hamstring Congressional Oversight?  \nA Case Study of the Government Accountability Office 4  \nBibliography 30  \nChapter 2 . The Republican Revolution in High Resolution  \nThe Heterogeneous Effects of the Republican Revolution on Oversight at the Government Accountability Office 32  \nBibliography 44  \nChapter 3 . Leaving No Variance on the Table:  \nCausal Machine Learning for Average and Conditional Effects in Cross-Sectional Data 45  \nBibliography 77  \nDiscussion 79  \nBibliography 82  \nAppendix A. Supplemental Material for Did the ","cbCaisBocS1oJA3I","https://ap.wps.com/l/cbCaisBocS1oJA3I","pdf",3988129,1,124,"English","en",105,"# Introduction\n## Research focus: congressional oversight and the Legislative State\n# Chapter 1: Did the Republican Revolution Hamstring Congressional Oversight?\n## Case study: Government Accountability Office\n# Chapter 2: The Republican Revolution in High Resolution\n## Heterogeneous effects on GAO oversight\n# Chapter 3: Leaving No Variance on the Table\n## Causal machine learning for average and conditional effects\n# Discussion\n# Appendix A: Supplemental Material\n# Appendix B: Supplemental Material\n# Appendix C: Supplemental Material","[{\"question\":\"What does the dissertation investigate about the 1994 Republican Revolution?\",\"answer\":\"It evaluates the effects of the 1994 Republican Revolution on congressional oversight capacity, using GAO oversight outputs as the focal setting.\"},{\"question\":\"What dataset and agency are central to the study?\",\"answer\":\"The dissertation builds a novel dataset compiling published, publicly available Government Accountability Office (GAO) materials, described as Congress’s “watchdog” oversight and auditing agency.\"},{\"question\":\"How are traditional causal inference and causal machine learning used in the dissertation?\",\"answer\":\"Chapter 1 estimates effects on multiple dependent variables with conventional causal inference; Chapter 2 uses a causal machine learning estimation procedure to examine heterogeneity; Chapter 3 expands on the broader use of causal machine learning in social science.\"}]","The Legislative State and the Republican Revolution: A Causal Machine Learning Approach - Dissertation | PDF",1785735913,312,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-legislative-state-and-the-republican-revolution-a-causal-machine-learning-approach-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@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/the-legislative-state-and-the-republican-revolution-a-causal-machine-learning-approach-dissertation/121492/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the dissertation investigate about the 1994 Republican Revolution?","Question",{"text":75,"@type":76},"It evaluates the effects of the 1994 Republican Revolution on congressional oversight capacity, using GAO oversight outputs as the focal setting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and agency are central to the study?",{"text":80,"@type":76},"The dissertation builds a novel dataset compiling published, publicly available Government Accountability Office (GAO) materials, described as Congress’s “watchdog” oversight and auditing agency.",{"name":82,"@type":73,"acceptedAnswer":83},"How are traditional causal inference and causal machine learning used in the dissertation?",{"text":84,"@type":76},"Chapter 1 estimates effects on multiple dependent variables with conventional causal inference; 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