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Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry
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In this paper, the authors highlight the coming age of Quantitative Legal Prediction with hopes that practicing lawyers, law students and law schools will take heed and prepare to survive (thrive) in this new ordering.Abstract:
Do I have a case? What is our likely exposure? How much is this going to cost? What will happen if we leave this particular provision out of this contract? How can we best staff this particular legal matter? These are core questions asked by sophisticated clients such as general counsels as well as consumers at the retail level. Whether generated by a mental model or a sophisticated algorithm, prediction is a core component of the guidance that lawyers offer. Indeed, it is by generating informed answers to these types of questions that many lawyers earn their respective wage. Every single day lawyers and law firms are providing predictions to their clients regarding their prospects in litigation and the cost associated with its pursuit (defense). How are these predictions being generated? Precisely what data or model is being leveraged? Could a subset of these predictions be improved by access to outcome data in a large number of 'similar' cases. Simply put, the answer is yes. Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely increase as greater access to appropriate legal data becomes available. This article is dedicated to highlighting the coming age of Quantitative Legal Prediction with hopes that practicing lawyers, law students and law schools will take heed and prepare to survive (thrive) in this new ordering. Simply put, most lawyers, law schools and law students are going to have to do more to prepare for the data driven future of this industry. In other words, welcome to Law's Information Revolution and yeah - there is going to be math on the exam.read more
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Neural legal judgment prediction in English
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References
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Journal ArticleDOI
The Cross‐Section of Expected Stock Returns
Eugene F. Fama,Kenneth R. French +1 more
TL;DR: In this paper, Bhandari et al. found that the relationship between market/3 and average return is flat, even when 3 is the only explanatory variable, and when the tests allow for variation in 3 that is unrelated to size.
Journal ArticleDOI
Fractional Brownian Motions, Fractional Noises and Applications
Journal ArticleDOI
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
TL;DR: This article used multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development, using the propensity score as a distinct matching variable.
Book ChapterDOI
Existence of an equilibrium for a competitive economy
Kenneth J. Arrow,Gerard Debreu +1 more
TL;DR: In this article, a simplification of the structure of the proofs has been made possible through use of the concept of an abstract economy, a generalization of that of a game, and proofs of the existence of an equilibrium are given for an integrated model of production, exchange and consumption.
Journal ArticleDOI
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
TL;DR: In this article, five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment, and these subclasses are applied within sub-populations, and model-based adjustments are then used to provide estimates for treatment effects within these sub-population.