Deep IV in Law

Zong Cang Zhang, Xinyue Zhang, Ruofan Wang and Daniel L. Chen
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Zong Cang Zhang: TUFTS UNIVERSITY USA - Partenaires IRSTEA - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture
Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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Abstract: Do US Circuit Courts' decisions on criminal appeals influence sentence lengths imposed by US District Courts? This Element explores the use of high-dimensional instrumental variables to estimate this causal relationship. Using judge characteristics as instruments, this Element implements two-stage models on court sentencing data for the years 1991 through 2013. This Element finds that Democratic, Jewish judges tend to favor criminal defendants, while Catholic judges tend to rule against them. This Element also finds from experiments that prosecutors backlash to Circuit Court rulings while District Court judges comply. Methodologically, this Element demonstrates the applicability of deep instrumental variables to legal data.

Keywords: Legal data; Causal inference; Natural language processing; Machine learning; Deep IV (search for similar items in EconPapers)
Date: 2022-08
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Published in Zhe Huang; Xinyue Zhang; Ruofan Wang; Daniel L Chen. Cambridge University Press, 2022, 9781009296403. ⟨10.1017/9781009296403⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03894065

DOI: 10.1017/9781009296403

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