[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122818-en":3,"doc-seo-122818-105":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},122818,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Semiparametric Methods for Two Problems in Causal Inference using Machine Learning - Doctor of Philosophy Dissertation","Semiparametric methods provide statistical guarantees for causal mechanisms in settings dominated by observational data with complex interactions, where machine learning models offer flexibility but also face bias and black-box inferential challenges. The dissertation develops two doubly-robust frameworks that exploit powerful nonparametric regression while targeting identifiable parametric components. It introduces kernel-convolved regression and location-scale score estimation to achieve efficiency guarantees, then proposes a label-merging categorical conditional independence test with double bootstrap calibration. Simulation results support improved power in high-dimensional regimes with controlled error.","Semiparametric Methods for Two Problems in Causal Inference using Machine Learning  \nHarvey Carter Klyne  \nStatistical Laboratory  \nDepartment of Pure Mathematics and Mathematical Statistics  \nUniversity of Cambridge  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \nEmmanuel College June 2023  \nDeclaration  \nThis thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or anyother University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee. Chapters 2 and 3 are joint work with Rajen Shah (University of Cambridge) . Chapter 2 is currently being submitted for publication as Klyne and Shah (2023) . We hope to submit Chapter 3 for publication soon.  \nHarvey Carter Klyne June 2023  \nAbstract  \nSemiparametric Methods for Two Problems in Causal Inference  \nusing Machine Learning  \nHarvey Carter Klyne  \nScientific applications such as personalised (precision) medicine require statistical guarantees on causal mechanisms, however in many settings only observational data with complex underlying interactions are available. Recent advances in machine learning have made it possible to model such systems, but their inherent biases and black-box nature pose an inferential challenge. Semiparametric methods are able to nonetheless leverage these powerful nonparametric regression procedures to provide valid statistical analysis on interesting parametric components of the data generating process.  \nThis thesis consists of three chapters. The first chapter summarises the semiparametric and causal inference literatures, paying particular attention to doubly-robust methods and conditional independence testing. In the second chapter, we explore the doubly-robust estimation of the average partial effect—a generalisation of the linear coefficient in a (partially) linear model and a local measure of causal effect. This framework involves two plug-in nuisance function estimates, and trades their errors off against each other. The first nuisance function is the conditional expectation function, whose estimate is required to be differentiable. We propose convolving an arbitrary plug-in machine learning regression—which need not be differentiable—with a Gaussian kernel, and demonstrate that for a range of kernel bandwidths we can achieve the semiparametric efficiency bound at no asymptotic cost to the regression mean-squared error. The second nuisance function is the derivative of the log-density of the predictors, termed the score function. This score function does not depend on the conditional distribution of the response given the predictors. Score estimation is only well-studied in the univariate case. We propose using a location-scale model to reduce the problem of multivariate score estimation to conditional mean and variance estimation plus univariate score estimation. This enables the use of an arbitrary machine learning regression. Simulations confirm the desirable properties of our  \napproaches, and code is made available in the R package drape (Doubly-Robust Average Partial Effects) available from [https://github.com/harveyklyne/drape](https://github.com/harveyklyne/drape).  \nIn the third chapter, we consider testing for conditional independence of two discrete random variables X and Y given a third continuous variable Z. Conditional independence testing forms the basis for constraint-based causal structure learning, but it has been shown that any test which controls size for all null distributions has no power against any alternative. For this reason it is necessary to restrict the null space, and it is","cbCais5TGKNH0BNd","https://ap.wps.com/l/cbCais5TGKNH0BNd","pdf",2876492,1,155,"English","en",105,"# Abstract\n# Chapter 1: Literature overview\n## Doubly-robust methods\n## Conditional independence testing\n# Chapter 2: Doubly-robust average partial effect\n## Plug-in nuisance function estimation\n## Kernel convolving for differentiability\n## Score function estimation for multivariate predictors\n# Chapter 3: Testing conditional independence\n## Constraint-based causal structure learning\n## Generalised covariance and high-dimensional testing\n## Greedy label merging\n## Double bootstrap calibration","[{\"question\":\"Why do semiparametric methods matter for causal inference with machine learning?\",\"answer\":\"They leverage flexible nonparametric regression to obtain valid statistical analysis on parametric components of the data generating process, even when only observational data and black-box learners are available.\"},{\"question\":\"How does the dissertation estimate nuisance functions in the doubly-robust average partial effect framework?\",\"answer\":\"One nuisance is the conditional expectation function, estimated using a kernel-convolution approach to enable differentiability while preserving regression accuracy; the other nuisance is the score function, handled via a location-scale model to reduce multivariate score estimation.\"},{\"question\":\"What is the core idea behind the conditional independence test in the final chapter?\",\"answer\":\"It constructs a generalised covariance measure using machine learning, then tests conditional correlation by checking whether an asymptotically Gaussian vector has mean zero; it improves power by greedily merging discrete labels and calibrates with a double bootstrap.\"}]","Semiparametric Methods for Two Problems in Causal Inference using Machine Learning - Doctor of Philosophy Dissertation | PDF",1785813065,391,{"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},"semiparametric-methods-for-two-problems-in-causal-inference-using-machine-learning-doctor-of-philosophy-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/semiparametric-methods-for-two-problems-in-causal-inference-using-machine-learning-doctor-of-philosophy-dissertation/122818/",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","2026-08-04",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},"Why do semiparametric methods matter for causal inference with machine learning?","Question",{"text":75,"@type":76},"They leverage flexible nonparametric regression to obtain valid statistical analysis on parametric components of the data generating process, even when only observational data and black-box learners are available.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation estimate nuisance functions in the doubly-robust average partial effect framework?",{"text":80,"@type":76},"One nuisance is the conditional expectation function, estimated using a kernel-convolution approach to enable differentiability while preserving regression accuracy; the other nuisance is the score function, handled via a location-scale model to reduce multivariate score estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the core idea behind the conditional independence test in the final chapter?",{"text":84,"@type":76},"It constructs a generalised covariance measure using machine learning, then tests conditional correlation by checking whether an asymptotically Gaussian vector has mean zero; it improves power by greedily merging discrete labels and calibrates with a double bootstrap.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]