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Series:   Elements in Law, Economics and Politics

Deep IV in Law

Appellate Decisions and Texts Impact Sentencing in Trial Courts

Published online by Cambridge University Press:  12 August 2022

Zhe Huang ,
Xinyue Zhang ,
Ruofan Wang  and
Daniel L. Chen
Zhe Huang
Affiliation:
Tufts University, Massachusetts
Xinyue Zhang
Affiliation:
OpenX
Ruofan Wang
Affiliation:
Microsoft
Daniel L. Chen
Affiliation:
Toulouse 1 Capitole University

Summary

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.
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Keywords

legal datacausal inferencenatural language processingmachine learningDeep IV
Type
Element
Information
Series: Elements in Law, Economics and Politics
Online ISBN: 9781009296403
Publisher: Cambridge University Press
Print publication: 25 August 2022

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References

Angrist, J., Imbens, G., and Rubin, D. (1996). “Identification of causal effects using instrumental variables,” Journal of Econometrics 71(1–2), 145–160.Google Scholar
Arora, S., Liang, Y., and Ma, T. (2017). “A simple but tough-to-beat baseline for sentence embeddings,” International Conference on Learning Representations. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C9&q=ICLR+simple+baseline&btnG=#d=gs_cit&t=1654538076793&u=%2Fsch-olar%3Fq%3Dinfo%3AXHz21aRyb6UJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3DenGoogle Scholar
Athey, S., Tibshirani, J., and Wager, S. (2019). “Generalized random forests” Annals of Statistics 47(2), 1148–1178.Google Scholar
Bakhturina, E., Barry, N., Buchanan, L., and Chen, D. L. (2016). “Events unrelated to crime predict criminal sentence length.” May 1.CrossRefGoogle Scholar
Becker, S. O. (2016). “Using instrumental variables to establish causality,” IZA World of Labor. April 1. https://wol.iza.org/articles/using-instrumental-variables-to-establish-causality/long.Google Scholar
Benabou, R., and Tirole, J. (2011). “Law and norms,” NBER Working Paper No. 17579.Google Scholar
Bengio, Y. , Schwenk, H., Senécal, J.-S., Morin, F., and Gauvain, J.-L. (2000). “A neural probabilistic language models,” Advances in Neural Information Processing Systems 13.Google Scholar
Benson, B. L., and Zimmerman, P. R. (2010). Handbook on the economics of crime. Edward Elgar.CrossRefGoogle Scholar
Bhuller, M., and Sigstad, H. (2022). “Feedback and Learning: The Causal Effects of Reversals on Judicial Decision-Making.” Available at SSRN 4000424.Google Scholar
Bhuller, Manudeep and Sigstad, Henrik. (2021). Feedback and Learning: The Causal Effects of Reversals on Judicial Decision-Making (January 4, 2022). Available at SSRN: https://ssrn.com/abstract=4000424 or http://dx.doi.org/10.2139/ssrn.4000424CrossRefGoogle Scholar
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.Google Scholar
Chen, Daniel L. and Frankenreiter, J.. (2021). Judicial Compliance in District Courts. Available at SSRN: https://ssrn.com/abstract=4058024 or http://dx.doi.org/10.2139/ssrn.4058024Google Scholar
Cameron, C.M. (1993). New avenues for modeling judicial politics. In Prepared for delivery at the Conference on the Political Economy of Public Law, Rochester, NY.Google Scholar
Chen, D. L., Frankenreiter, J., and Yeh, S. (2017). “Judicial compliance in district courts.” https://ssrn.com/abstract=2740594.Google Scholar
Chen, Daniel L., and Loecher, Markus (2019). “Mood and the malleability of moral reasoning.” Available at SSRN 2740485.Google Scholar
Chen, D. L., Levonyan, V., and Yeh, S. (2016). “Do policies affect preferences? Evidence from random variation in abortion jurisprudence,” TSE Working Paper No. 16-723.Google Scholar
Chernozhukov, V., Chetverikov, D., Demirer, M. et al. (2016). “Double/debiased machine learning for treatment and causal parameters.” arXiv:1608.00060.Google Scholar
Collobert, R., and Weston, J. (2008). “A unified architecture for natural language processing: Deep neural networks with multitask learning,” Proceedings of the 25th International Conference on Machine Learning, 5 July, 160–167.Google Scholar
Cortes, C. , and Vapnik, V. (1995). “Support-vector networks,” Machine Learning 20(3), 273–297.Google Scholar
Cox, D. R. (1958). “The regression analysis of binary sequences,” Journal of the Royal Statistical Society: Series B (Methodological) 20(2), 215–232.Google Scholar
Dippel, C. , Gold, R., Heblich, S., and Pinto, R. (2017). “Instrumental variables and causal mechanisms: Unpacking the effect of trade on workers and voters,” NBER Working Paper No. 23209.Google Scholar
Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., and Stewart, B. M. (2017). “How to make causal inferences using texts.” arXiv:1802.02163.Google Scholar
Fearn, N. E. (2007). “A multilevel analysis of community effects on criminal sentencing,” Justice Quarterly 22(4), 452–487. https://doi.org/10.1080/07418820500364668.Google Scholar
Friedman, J. H. (2001). “Greedy function approximation: A gradient boosting machine,” Annals of Statistics 29(5), 1189–1232.CrossRefGoogle Scholar
Hartford, J., Lewis, G., Leyton-Brown, K., and Taddy, M. (2017). “Deep IV: A flexible approach for counterfactual prediction,” Proceedings of the 34th International Conference on Machine Learning, PMLR 70, 1414–1423.Google Scholar
Huang, W. S., Finn, M. A., Ruback, R. B., and Friedmann, R. R. (1996). “Individual and contextual influences on sentence lengths: Examining political conservatism,” The Prison Journal 76, 398–419.CrossRefGoogle Scholar
Ioffe, S., and Szegedy, C. (2015). “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proceedings of the 32nd International Conference on Machine Learning, 448–456.Google Scholar
Kautt, P. M. (2002). “Location, location, location: Interdistrict and intercircuit variation in sentencing outcomes for federal drug-trafficking offenses,” Justice Quarterly 19(4), 633–671. https://doi.org/10.1080/07418820200095381.Google Scholar
Kornhauser, Lewis A. (1993) “The normativity of law.” American Law and Economics Review 1, 3.Google Scholar
Le, Q., and Mikolov, T. (2014). “Distributed representations of sentences and documents.” arXiv:1405.4053.Google Scholar
Lewis, G., and Syrgkanis, V. (2018). “Adversarial generalized method of moments." arXiv:1803.07164.Google Scholar
Liaw, A., and Wiener, M. (2002). “Classification and regression by randomForest.” R News 2, 18–22.Google Scholar
Microsoft Research (2019). “EconML: A Python package for ML-based heterogeneous treatment effects estimation,” Version 0.x. https://github.com/microsoft/EconML.Google Scholar
Mikolov, T. , Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). “Distributed representations of words and phrases and their compositionality,” Advances in Neural Information Processing Systems 26, 3111–3119.Google Scholar
Mnih, A., and Hinton, G. E. (2008). “A scalable hierarchical distributed language model,” Advances in Neural Information Processing Systems 21, 1081–1088.Google Scholar
Oprescu, M., Syrgkanis, V., and Wu, Z. S. (2019). “Orthogonal random forest for causal inference,” Proceedings of the 36th International Conference on Machine Learning.Google Scholar
Paszke, A., Gross, S., Massa, F. et al. (2019). “PyTorch: An imperative style, high-performance deep learning library,” Advances in Neural Information Processing Systems 32, 8024–8035 http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.Google Scholar
Posner, Richard A (1973). “An economic approach to legal procedure and judicial administration.” The Journal of Legal Studies 2(2), 399–458.Google Scholar
Quinlan, J. R. (1986). “Induction of decision trees,” Machine Learning 1, 81–106.Google Scholar
Rehurek, R., and Sojka, P. (2010). “Software framework for topic modelling with large corpora,” Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks.Google Scholar
Schmitt, C. (1969). Gesetz und Urteil: Eine Untersuchung zum Problem der Rechtspraxis. Munich Beck.Google Scholar
Schmitt, C. (1985 [1923]). The crisis of parliamentary democracy. Trans. E. Kennedy. MIT Press.Google Scholar
Schmitt, C. (2005). Political theology: Four chapters on the concept of sovereignty. Trans. G. Schwab. University of Chicago Press.Google Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research 15(1), 1929–1958.Google Scholar
Stepner, M. (2014). “BINSCATTER: Stata module to generate binned scatterplots.” https://michaelstepner.com/binscatter/binscatter-StataConference2014.pdfGoogle Scholar
Turian, J., Ratinov, L., and Bengio, Y. (2010). “Word representations: A simple and general method for semi-supervised learning,” Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics 384–394.Google Scholar
Van der Maaten, L., and Hinton, G. (2008). “Visualizing data using t-SNE,” Journal of Machine Learning Research, 9(11).Google Scholar

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