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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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.