Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora

Elliott Ash, Daniel L. Chen, Raul Delgado, Eduardo Fierro and Shasha Lin

No 18-90, IAST Working Papers from Institute for Advanced Study in Toulouse (IAST)

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.

Date: 2018-08
New Economics Papers: this item is included in nep-big and nep-law
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Working Paper: Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:iastwp:33154

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