Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora

Ash, Elliott, Chen, Daniel L., Delgado, Raul, Fierro, Eduardo and Lin, Shasha (2018) Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora. TSE Working Paper, n. 18-977, Toulouse

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Official URL: http://tse-fr.eu/pub/33153


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.

Item Type: Monograph (Working Paper)
Language: English
Date: August 2018
Place of Publication: Toulouse
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse Capitole
Site: UT1
Date Deposited: 21 Dec 2018 11:02
Last Modified: 21 Dec 2018 11:02
OAI ID: oai:tse-fr.eu:33153
URI: http://publications.ut-capitole.fr/id/eprint/28404

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