Learning Policy Levers: Toward Automated Policy Classification Using Judicial Corpora

26 Pages Posted: 11 Jan 2019

See all articles by Elliott Ash

Elliott Ash

ETH Zürich

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Raúl Delgado-Wise

Universidad Autónoma de Zacatecas

Eduardo Fierro

New York University (NYU), Students

Shasha Lin

IBM

Date Written: August 10, 2018

Abstract

To build inputs for end-to-end machine learning estimates of the causal impacts of law, we consider the problem of automatically classifying cases by their policy impact. We propose and implement a semi-supervised multi-class learning model, with the training set being a hand-coded dataset of thousands of cases in over 20 politically salient policy topics. Using opinion text features as a set of predictors, our model can classify labeled cases by topic correctly 91% of the time. We then take the model to the broader set of unlabeled cases and show that it can identify new groups of cases by shared policy impact.

Suggested Citation

Ash, Elliott and Chen, Daniel L. and Delgado-Wise, Raúl and Fierro, Eduardo and Lin, Shasha, Learning Policy Levers: Toward Automated Policy Classification Using Judicial Corpora (August 10, 2018). Available at SSRN: https://ssrn.com/abstract=3308961 or http://dx.doi.org/10.2139/ssrn.3308961

Elliott Ash

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen (Contact Author)

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

Raúl Delgado-Wise

Universidad Autónoma de Zacatecas ( email )

Jardín Juárez #147
Centro Histórico
Zacatecas, 98000
Mexico

Eduardo Fierro

New York University (NYU), Students ( email )

Shasha Lin

IBM ( email )

United States

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