We present the methodology for developing a predictive model for identifying homeless persons likely to have high future costs for public services. It was developed by linking administrative records from 2007 through 2012 for seven Santa Clara County agencies and identifying 38 demographic,...

Two new predictive screening tools that are based on analyzing records of over one-million people who experienced homelessness have been placed in the public domain by the Economic Roundtable. The two groups targeted by these tools are low-wage workers who have just lost their jobs and youth...

We use administrative microdata and statistical learning methods to analyze how personal characteristics and the consumption of healthcare services help predict the first-time receipt of "long-term care allowance" (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that...

To their credit, empirical legal scholars try to live up to the highest methodological standards from the social sciences. But these standards do not always match the legal research question. This paper focuses on normative legal argument based on empirical evidence. Whether there is a normative...

When the beach patrol raises the alarm that a shark has been sighted we know what to do, but how should we respond to an alarm that is based on predictions of what will happen 100 years from now and the person raising the alarm tells us we must make great sacrifices now to avoid the predicted...

Legal scholarship on behavioralism and the implications of cognitive biases for the law is flourishing. In parallel with the rise of such commentary, legal scholars have begun to discuss the role of the emotions in legal discourse. Discussion turns on the appropriateness of various emotions for...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

We study the incentives of drug producers to develop predictive biomarkers, taking into account strategic interaction between drug producers and health plans. For this purpose we develop a two-dimensional spatial framework that allows us to capture the informational role of biomarkers and their...