IDEAS home Printed from https://ideas.repec.org/p/tse/wpaper/33153.html
   My bibliography  Save this paper

Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora

Author

Listed:
  • Ash, Elliott
  • Chen, Daniel L.
  • Delgado, Raul
  • Fierro, Eduardo
  • Lin, Shasha

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 & Chen, Daniel L. & Delgado, Raul & Fierro, Eduardo & Lin, Shasha, 2018. "Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora," TSE Working Papers 18-977, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:33153
    as

    Download full text from publisher

    File URL: https://users.nber.org/~dlchen/papers/Learning_Policy_Levers.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tse:wpaper:33153. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tsetofr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.