With the Amanda Knox trial re-engaged in Italy, an op-ed in today's New York Times, "Justice Flunks Math," underscores a regrettable point: Lawyers' (and judges') unfamiliarity with even basic probability theory can fuel "grave errors of law" (not to mention casts even more unflattering light onto the legal profession generally). The op-ed authors even manage to rough-up one of Lawrence Tribe's (Harvard) early articles where he laments the "overbearing impressiveness" of numbers used in the trial context.
A pair of recent posts (and related videos) on the Stata Blog involving multilevel modeling warrants note. Part 1 (and related video) and Part 2 (and video) walk readers through such topics as hierarchical or multilevel data as well as how to analyze longitudinal data with Stata’s xtmixed command. Insofar as many empirical legal projects involve data structurally nested within counties and/or states, circuits, etc., multilevel techniques are quite important.
As you've probably heard, the U.S. News 2014 Law School Rankings are out. Rather than offer commentary, I thought I'd piggyback on Paul Caron's useful post comparing the overall rankings with the peer reputation rankings. So here, for your edification, are the numbers Paul compiled in scatterplot form. (PDF)
Information on two separate (Main and Advanced) Causal Inference Workshops at Northwestern Law School this summer follows.Both workshops will be taught by world-class
causal inference researchers. Registration for each is limited to 100 participants.
Main workshop: Monday – Friday,
June 24-28, 2013
Advanced workshop: Monday - Wednesday,
August 12-14, 2013.
For information and to register for either or both workshops: (click here)
Overview and Target Audience: Most empirical methods
courses survey a variety of methods. We will begin instead with the goal of
causal inference, and discuss how to design research to come closer to that
goal. The methods are often adapted to a particular study. Some of the methods
we will discuss are covered in PhD programs, but rarely in depth, and rarely
with a focus on causal inference and on which methods to prefer for messy,
real-world datasets with limited sample sizes. Each day will include with a
Stata “workshop” to illustrate selected methods with real data and Stata
code. We will
assume knowledge, at the level of an upper-level college econometrics or similar
course,of multivariate regression, including OLS, logit, and probit;
basic probability and statistics including conditional and compound
probabilities, confidence intervals, t-statistics, and standard errors; and some
understanding of instrumental variables.
Advanced Workshop Overview and Target
advanced workshop seeks to provide an in-depth discussion of selected topics at
the causal inference research frontier.Our target
audience is empirical researchers who are familiar with the basics of causal
inference (from our main workshop or otherwise), and want to extend their
Now that Cass Sunstein (Harvard) has departed the Obama Administration (and OIRA) and migrated back to academic life, in a recent paper published by the University of Chicago Law Review, Empirically Informed Regulation, Sunstein illustrates the central role data play (or, at least should play) in the development of regulations, with an emphasis on behavioral economics. The paper's abstract follows.
"In recent years, social scientists have been incorporating empirical findings about human behavior into economic models. These findings offer important insights for thinking about regulation and its likely consequences. They also offer some suggestions about the appropriate design of effective, low-cost, choice-preserving approaches to regulatory problems, including disclosure requirements, default rules, and simplification. A general lesson is that small, inexpensive policy initiatives can have large and highly beneficial effects. In the United States, a large number of recent practices and reforms reflect an appreciation of this lesson. They also reflect an understanding of the need to ensure that regulations have strong empirical foundations, both through careful analysis of costs and benefits in advance and through retrospective review of what works and what does not."