[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117209-en":3,"doc-seo-117209-105":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},117209,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Towards Reliable Causal Machine Learning for Macroeconomics","The dissertation investigates how machine learning can contribute to macroeconomic policy-making by addressing prediction and causal inference challenges. It begins with a case study of income prediction from Icelandic tax data, demonstrating both quantitative and qualitative usefulness. The work then focuses on observational causal inference under covariate shift using density-ratio debiasing and balancing-weights duality, extending to numerical equivalence results for debiased learning estimators. Finally, it develops sensitivity analysis for settings with unobserved confounders, including dynamic cases using reinforcement learning frameworks.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nTowards Reliable Causal Machine Learning for Macroeconomics  \nPermalink  \n[https://escholarship.org/uc/item/4gv346qr](https://escholarship.org/uc/item/4gv346qr)  \nAuthor  \nBruns-Smith, David Alexander  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nTowards Reliable Causal Machine Learning for Macroeconomics  \nBy  \nDavid A Bruns-Smith  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy  \nin  \nComputer Science  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Avi Feller, Co-chair Professor Jacob Steinhardt, Co-chair Professor Emi Nakamura  \nSummer 2024  \nTowards Reliable Causal Machine Learning for Macroeconomics  \nCopyright 2024  \nBy  \nDavid A Bruns-Smith  \n1  \nAbstract  \nTowards Reliable Causal Machine Learning for Macroeconomics  \nBy  \nDavid A Bruns-Smith  \nDoctor of Philosophy in Computer Science  \nUniversity of California, Berkeley  \nProfessor Avi Feller, Co-chair  \nProfessor Jacob Steinhardt, Co-chair  \nThe 21st century has seen an explosion in the availability of economic data, and machine learning tools for making predictions from that data. Motivated by these developments, in this dissertation, I consider the broad question of: what, if anything, can machine learning contribute to macroeconomic policy-making? In Chapter 2, I begin with a case study of a pure prediction problem in Icelandic tax data, and show that machine learning is quantitatively and qualitatively useful for this problem. But in economic policy settings, we want to predict the effect of an intervention, a much more challenging problem than the standard supervised learning task. Therefore, the rest of my dissertation focuses on using machine learning for observational causal inference. In Part 2, I consider the “no unobserved confounders” case, where we assume that we observe all of the relevant covariates. In this setting, the causal inference problem reduces to a prediction task under covariate shift, and we can debias causal effect estimates using the density ratio-the object that measures how the covariate distributions shift. In high dimensions, density ratios are typically not well behaved, and I help make progress on this front in Chapter 3 by drawing connections between density ratio estimation and so-called “balancing weights” estimators via a duality argument. Then in Chapter 4, I apply these results to obtain a broad set of numerical equivalence results for debiased machine learning estimators, which results in a number of implications for undersmoothing and hyperparameter tuning in practice. In Part 3, I turn to the setting where we do potentially have unobserved confounders, making unbiased recovery of the causal effect impossible. Instead, we use “sensitivity analysis”, which measures how quickly the estimated causal relationship degrades with hypothetical confounding. Of particular relevance to macroeconomics, I develop algorithms for the dynamic setting where causal effects unroll over time, adopting the Reinforcement Learning framework. Chapter 5 considers the tabular setting, and Chapter 6 extends these results to function approximation with machine learning.  \ni  \nContents  \nContents i  \nList of Figures iv  \nList of Tables vii  \nI Prediction in Macroeconomics 1  \n1 Introduction 2  \n1.1 Causal Inference and Machine Learning in Macroeconomics .......... 2  \n1.2 Overview of this Dissertation .......................... 3  \n2 Case Study: Income Prediction 8  \n2.1 Introduction .................................... 8  \n2.2 Related Work ................................... 10  \n2.3 Defining Income Shocks ............................. 12  \n2.4 The Income Prediction Problem ......................... 15  \n2.5 Shocks .......................","cbCaijLe1YuCj91p","https://ap.wps.com/l/cbCaijLe1YuCj91p","pdf",4678094,1,193,"English","en",105,"# I Prediction in Macroeconomics\n## 1 Introduction\n## 2 Case Study: Income Prediction\n# II Causal Inference with No Unobserved Confounders\n## 3 Duality for Balancing Weights\n## 4 Augmented Balancing Weights as Undersmoothing\n# III Causal Inference with Unobserved Confounders\n## 5 Dynamic Sensitivity Analysis: The Tabular Case\n## 6 Dynamic Sensitivity Analysis: The General Case","[{\"question\":\"What central question does the dissertation address about macroeconomic policy-making?\",\"answer\":\"It asks what machine learning can contribute to macroeconomic policy-making, moving from prediction to the more demanding goal of estimating intervention effects.\"},{\"question\":\"How does the dissertation handle causal inference when there are no unobserved confounders?\",\"answer\":\"It assumes all relevant covariates are observed, reducing causal inference to prediction under covariate shift and using density-ratio ideas to debias causal effect estimates.\"},{\"question\":\"What approach is used when unobserved confounders may exist?\",\"answer\":\"It employs sensitivity analysis, quantifying how estimated causal relationships degrade under hypothetical levels of unobserved confounding, including dynamic settings treated with reinforcement learning.\"}]","Towards Reliable Causal Machine Learning for Macroeconomics | 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central question does the dissertation address about macroeconomic policy-making?","Question",{"text":75,"@type":76},"It asks what machine learning can contribute to macroeconomic policy-making, moving from prediction to the more demanding goal of estimating intervention effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation handle causal inference when there are no unobserved confounders?",{"text":80,"@type":76},"It assumes all relevant covariates are observed, reducing causal inference to prediction under covariate shift and using density-ratio ideas to debias causal effect estimates.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is used when unobserved confounders may exist?",{"text":84,"@type":76},"It employs sensitivity analysis, quantifying how estimated causal relationships degrade under hypothetical levels of unobserved confounding, including dynamic settings treated with reinforcement 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