The goal of the Interdisciplinary Seminar in Quantitative Methods is to provide an interdisciplinary environment where researchers can present and discuss cutting-edge research in quantitative methodology. The talks will be aimed at a broad audience, with more emphasis on conceptual than technical issues. The research presented will be varied, ranging from new methodological developments to applied empirical papers that use methodology in an innovative way. We welcome speakers and audiences from all fields in the social, natural, and behavioral sciences.
Location: Eldersveld Room, 5670 Haven Hall
Time: Wednesdays, 4:00 - 5:30pm
Note: Please see event listing for particular changes in location or time.
September 10, 2014: Scott Page, Complex Systems, Political Science, and Economics, University of Michigan
September 24, 2014: Timothy J. Vogelsang, Economics, Michigan State University
October 8, 2014: Simon Jackman, Political Science, Stanford University
October 22, 2014: Mel Stephens, Economics, University of Michigan
November 5, 2014: Kevin M. Quinn, School of Law, University of California at Berkeley
November 19, 2014: Thomas D. Cook, Sociology, Psychology, and Education and Social Policy, Northwestern University
December 3, 2014: Neal Beck, Department of Politics, New York University
February 11, 2015: Don Rubin, Statistics, Harvard University
February 25, 2015: William G. Jacoby, Political Science, Michigan State University
March 11, 2015: Tyler J. VanderWeele, Epidemiology and Biostatistics, Harvard School of Public Health
March 25, 2015: Elizabeth A. Stuart, Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health
April 8, 2015: Luke Keele, Political Science, Penn State
April 22, 2015: Dylan Small, Statistics, Wharton School, University of PennsylvaniaAdd to Your Google Calendar
An effect modifier is a pretreatment covariate such that the magnitude of the treatment effect or its stability changes with the level of the covariate. Generally, other things being equal, larger treatment effects and less heterogeneous treatment effects are less sensitive to unmeasured biases in observational studies. It is known that when there is effect modification, an overall test that ignores an effect modifier may report greater sensitivity to unmeasured bias than a test that combines results at different levels of the effect modifier. This known combined test reports that there is evidence of an effect somewhere that is insensitive to bias of a certain magnitude, but it does not draw inferences about affected subgroups. If there is effect modification, one would like to identify specific subgroups for which there is evidence of effect that is insensitive to small or moderate biases. We propose an exploratory method for discovering effect modification combined with a confirmatory method of simultaneous inference that strongly controls the family-wise error rate in a sensitivity analysis, despite the fact that the groups being compared are defined empirically. A new form of matching, strength k matching, permits a search through more than k covariates for effect modifiers, yet no pairs are lost providing at most k covariates are selected to group the pairs. In a strength k match, each set of k covariates is exactly balanced, though a set of > k covariates may exhibit imbalance. We apply the method to study the effects of the powerful earthquake that struck Chile in 2010. This is joint work with Jesse Hsu, Jose Zubizarreta and Paul Rosenbaum.
May 6, 2015: Bryan S. Graham, Economics, University of California at Berkeley