Bernard Black

FINANCE
Nicholas J. Chabraja Professor, Northwestern University Law School
Professor of Finance (Courtesy)

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Bernard S. Black is Professor of Finance at Kellogg School of Management, and will be Nicholas D. Chabraja Professor at Northwestern University School of Law and Kellogg School of Management (beginning Sept. 1, 2010). He is also managing director of the Social Science Research Network, and founding chairman of the annual Conference on Empirical Legal Studies. Professor Black received a B.A. from Princeton University, an M.A. in physics from University of California at Berkeley and a J.D. from Stanford Law School. He was Professor of Law at Stanford Law School from 1998-2004 and at Columbia Law School from 1988-1998. His principal research areas are law and finance, international corporate governance, health care and medical malpractice, and corporate and securities law. His books include To Sue is Human: A Profile of Medical Malpractice Litigation (forthcoming 2010, with David Hyman, William Sage, Charles Silver, and Kathryn Zeiler), The Law and Finance of Corporate Acquisitions (2nd ed., with Ronald Gilson, 1995 and supplement 2003) and Guide to the Russian Law on Joint Stock Companies (with Reinier Kraakman and Anna Tarassova (1998).

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Doctoral
Introduction to Applied Econometrics III: Research Design for Causal Inference (MECS-478-0)
Much empirical research stands or falls on whether it provides a credible basis for causal inference: Does a change in the predictor variable x cause a change in an outcome variable y. This course introduces methods for credible causal inference, emphasizing intuition and hands-on work with real data. It follows Applied Econometrics I and II, but should be accessible to students with other reasonable prior methods training (e.g., non-Kellogg graduate students). Topics include: Rubin causal model (causal inference as missing data problem); inference in randomized experiments; difference-in-differences (including triple differences; event studies; synthetic controls); regression discontinuity; the logic behind instrumental variables (you will already know the math); matching, propensity score weighting, and subclassification methods for observational studies; assessing and achieving covariate balance; continuous treatments; causal inference with panel data; treatment effect heterogeneity and local treatment effects; when to use (and not use) regression methods; Rosenbaum and Manski bounds.