[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118684-en":3,"doc-seo-118684-105":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},118684,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Rethinking Machine Learning for Heterogeneous Treatment Effect Estimation","Estimating the causal effect of a treatment or policy on an outcome is central across domains such as economics, marketing, and medicine. Moving from population average effects to personalization has driven rapid growth in machine-learning approaches for heterogeneous treatment effect estimation. This thesis addresses why method choice is difficult: individualized ground-truth effects are unobserved, so validation against held-out labels is unavailable. Through theoretical and empirical studies, it analyzes success and failure modes, evaluation, and model selection, then proposes new methodology and reevaluates research priorities while exploring multiple real-world extensions, including time-to-event data with censoring and competing events.","Rethinking Machine Learning for Heterogeneous Treatment Effect  \nEstimation  \nAlicia Marie Curth  \nClare Hall  \nSeptember 2024  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \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. It is not substantially the same as any work that has already been submitted, or is being concurrently submitted, for any 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.  \nAlicia Marie Curth  \nSeptember 2024  \nAbstract  \nThe need to estimate the causal effect that a treatment, policy or other intervention had on an outcome variable is a problem which ubiquitously appears in fields ranging from economics to marketing and medicine. Historically, population average effects have been the main estimand of interest, however, a growing focus on personalization has led to a recent surge in interest in using machine learning to enable heterogeneous treatment effect estimation and resulted in rapid expansion of the machine learning toolbox for this problem over the last decade. Because such effect estimates are often of interest in high-stakes environments, one needs to be sure that the best method from the ever-growing toolbox can be identified for a given application – however, this turns out to be much more difficult than in more standard machine learning problems. That is, in canonical supervised machine learning settings, choosing between different methods is relatively straightforward as their prediction performance can usually simply be evaluated against held-out labels on validation datasets. In the context of treatment effect estimation, however, there are no such labels because ground truth individualized treatment effects are never observed in practice – which substantially complicates the choice between available methods. Thus, the bottleneck for obtaining actionable personalized effect estimates in practice is no longer a lack in availability of good candidate estimators, but a lack of understanding when to use which of the many existing methods – and why.  \nWith the ultimate goal of providing new fundamental insights into heterogeneous treatment effect estimation as a machine learning problem, this thesis therefore aims to build deeper understanding of the machine learning challenges inherent to different classes of effect estimation problems through theoretical and empirical studies. We investigate the success-and failure modes of different approaches to effect estimation, model evaluation and model selection in this context, and provide new methodology that fills identified gaps where necessary. At a higher level, we also use the discovered insights to reevaluate the research priorities that have been set in the machine learning literature on the topic thus far and investigate important problem characteristics and research directions that have been relatively understudied.  \nWe explore four subproblems in detail that fall within the landscape of machine learning for heterogeneous treatment effect estimation. First, we study how to best design machine learning methods for estimating heterogeneous treatment effects, and theoretically and empirically  \ndemonstrate that methods that target treatment effects directly often perform better than those that simply predict outcomes under different treatment choices separately. We show that this can not only be achieved by using multi-stage estimators adapted from the statistics literature, but also by designing new deep learning architectures that can be trained in an end-to-end fashion. Second, we take a critical look at model evaluation practices in the machine learning literature on this top","cbCainDJKUXBIr2W","https://ap.wps.com/l/cbCainDJKUXBIr2W","pdf",14880612,1,244,"English","en",105,"# Abstract\n## Motivation and core challenge\n## Thesis goals and contributions\n## Four subproblems: estimation design, evaluation, selection, real-world complexities","[{\"question\":\"Why is choosing a machine-learning method for heterogeneous treatment effect estimation harder than in standard supervised learning?\",\"answer\":\"Because individualized treatment effects are never observed in practice, there are no held-out labels to validate predictive performance, making method comparison and selection substantially more complicated.\"},{\"question\":\"What does the thesis aim to achieve in heterogeneous treatment effect estimation?\",\"answer\":\"It seeks deeper fundamental insights into this problem as a machine-learning task by studying theoretical and empirical challenges, identifying success and failure modes, and providing new methodology where gaps are found.\"},{\"question\":\"What additional real-world data characteristics does the thesis consider beyond confounding-induced covariate shift?\",\"answer\":\"It studies time-to-event heterogeneous treatment effects with censoring and competing events, and longitudinal treatment effect estimation with informative sampling, showing these mechanisms create additional covariate shifts that must be handled.\"}]","Rethinking Machine Learning for Heterogeneous Treatment Effect Estimation | 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is choosing a machine-learning method for heterogeneous treatment effect estimation harder than in standard supervised learning?","Question",{"text":75,"@type":76},"Because individualized treatment effects are never observed in practice, there are no held-out labels to validate predictive performance, making method comparison and selection substantially more complicated.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the thesis aim to achieve in heterogeneous treatment effect estimation?",{"text":80,"@type":76},"It seeks deeper fundamental insights into this problem as a machine-learning task by studying theoretical and empirical challenges, identifying success and failure modes, and providing new methodology where gaps are found.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional real-world data characteristics does the thesis consider beyond confounding-induced covariate shift?",{"text":84,"@type":76},"It studies time-to-event heterogeneous treatment 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