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The Statistical Analysis of Judicial Decisions and Legal Rules with Classification Trees
Author(s) -
Kastellec Jonathan P.
Publication year - 2010
Publication title -
journal of empirical legal studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.529
H-Index - 24
eISSN - 1740-1461
pISSN - 1740-1453
DOI - 10.1111/j.1740-1461.2010.01176.x
Subject(s) - legal doctrine , judicial opinion , doctrine , decision tree , supreme court , confession (law) , political science , politics , scholarship , law , computer science , artificial intelligence
A key question in the quantitative study of legal rules and judicial decision making is the structure of the relationship between case facts and case outcomes. Legal doctrine and legal rules are general attempts to define this relationship. This article summarizes and utilizes a statistical method relatively unexplored in political science and legal scholarship—classification trees—that offers a flexible way to study legal doctrine. I argue that this method, while not replacing traditional statistical tools for studying judicial decisions, can better capture many aspects of the relationship between case facts and case outcomes. To illustrate the method's advantages, I conduct classification tree analyses of search and seizure cases decided by the U.S. Supreme Court and confession cases decided by the courts of appeals. These analyses illustrate the ability of classification trees to increase our understanding of legal rules and legal doctrine.