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Skip to Main Content Advertisement Journals Books * Search Menu * * * Menu * * Navbar Search Filter The Quarterly Journal of EconomicsThis issueB4 - Economic MethodologyC1 - Econometric and Statistical Methods and Methodology: General EconomicsBooksJournalsOxford Academic Mobile Enter search term Search * Issues * JEL * A - General Economics and Teaching * Browse content in A - General Economics and Teaching * A1 - General Economics * Browse content in A1 - General Economics * A11 - Role of Economics; Role of Economists; Market for Economists * B - History of Economic Thought, Methodology, and Heterodox Approaches * Browse content in B - History of Economic Thought, Methodology, and Heterodox Approaches * B4 - Economic Methodology * Browse content in B4 - Economic Methodology * B49 - Other * C - Mathematical and Quantitative Methods * Browse content in C - Mathematical and Quantitative Methods * C0 - General * Browse content in C0 - General * C00 - General * C01 - Econometrics * C1 - Econometric and Statistical Methods and Methodology: General * Browse content in C1 - Econometric and Statistical Methods and Methodology: General * C10 - General * C11 - Bayesian Analysis: General * C12 - Hypothesis Testing: General * C13 - Estimation: General * C14 - Semiparametric and Nonparametric Methods: General * C18 - Methodological Issues: General * C2 - Single Equation Models; 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Regional Labor Markets; Population; Neighborhood Characteristics R28 - Government Policy R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location R30 - General R31 - Housing Supply and Markets R38 - Government Policy R4 - Transportation Economics R40 - General R41 - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise R48 - Government Pricing and Policy Z - Other Special Topics Z1 - Cultural Economics; Economic Sociology; Economic Anthropology Z10 - General Z12 - Religion Z13 - Economic Sociology; Economic Anthropology; Social and Economic Stratification Browse all content Browse content in * More Content * Advance Articles * Editor's Choice * Submit * Author Guidelines * Submission Site * Open Access Options * Self-Archiving Policy * Why Submit? * Purchase * About * About The Quarterly Journal of Economics * Editorial Board * Advertising and Corporate Services * Journals Career Network * Alerts * Policies * Dispatch Dates * Contact Us Close Navbar Search Filter The Quarterly Journal of EconomicsThis issueB4 - Economic MethodologyC1 - Econometric and Statistical Methods and Methodology: General EconomicsBooksJournalsOxford Academic Enter search term Search Advanced Search Search Menu Article Navigation Close mobile search navigation Article Navigation Volume 139 Issue 2 May 2024 * < Previous * Next > Article Navigation Article Navigation Journal Article MACHINE LEARNING AS A TOOL FOR HYPOTHESIS GENERATION Get access Jens Ludwig, Jens Ludwig University of Chicago and National Bureau of Economic Research , United States Search for other works by this author on: Oxford Academic Google Scholar Sendhil Mullainathan Sendhil Mullainathan University of Chicago and National Bureau of Economic Research , United States E-mail: Sendhil.Mullainathan@chicagobooth.edu Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 139, Issue 2, May 2024, Pages 751–827, https://doi.org/10.1093/qje/qjad055 Published: 10 January 2024 Article history Published: 10 January 2024 Corrected and typeset: 22 January 2024 * Views * Article contents * Cite CITE Jens Ludwig, Sendhil Mullainathan, Machine Learning as a Tool for Hypothesis Generation, The Quarterly Journal of Economics, Volume 139, Issue 2, May 2024, Pages 751–827, https://doi.org/10.1093/qje/qjad055 Select Format Select format .ris (Mendeley, Papers, Zotero) .enw (EndNote) .bibtex (BibTex) .txt (Medlars, RefWorks) Download citation Close * Permissions Icon Permissions * Share Icon Share * Facebook * Twitter * LinkedIn * Email Navbar Search Filter The Quarterly Journal of EconomicsThis issueB4 - Economic MethodologyC1 - Econometric and Statistical Methods and Methodology: General EconomicsBooksJournalsOxford Academic Mobile Enter search term Search Close Navbar Search Filter The Quarterly Journal of EconomicsThis issueB4 - Economic MethodologyC1 - Econometric and Statistical Methods and Methodology: General EconomicsBooksJournalsOxford Academic Enter search term Search Advanced Search Search Menu ABSTRACT While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (e.g., race) or existing psychology research, nor are they already known (even if tacitly) to people or experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (e.g., cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science. JEL B4 - Economic Methodology C1 - Econometric and Statistical Methods and Methodology: General Issue Section: Article © The Author(s) 2024. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) You do not currently have access to this article. 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