PRIISM Seminars

The PRIISM seminar series consists of research seminars of interest to an applied statistics audience, from innovative applications of applied statistics to novel statistical theory and methodology. For an archive of past events, here (2008 - 2015) and here (2015 - 2018).  Links to related seminars are given here.

Upcoming and Recent Events: Fall 2018 and Spring 2019


Date, Time, LocationSpeaker Name, AffiliationTopic (click for more info)
10/24/2018 (Weds)
11:00 am - 12:00 pm,
Kimball 3rd Fl Conf Rm 
 Leslie McClure (Drexel)
Abstract: TBA
10/31/2018 (Weds)
10:30 am - 12:00 pm,
Kimball 3rd Fl Conf Rm 
 Chuck Huber (Stata Corp)
Abstract: This talk introduces the concepts and jargon of structural equation modeling (SEM) including path diagrams, latent variables, endogenous and exogenous variables, and goodness of fit. I demonstrate how to fit many familiar models such as linear regression, multivariate regression, logistic regression, confirmatory factor analysis, and multilevel models using -sem-. I wrap up by demonstrating how to fit structural equation models that contain both structural and measurement components.

11/14/2018 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

 Ravi Shroff (NYU)
Abstract: Policymakers often seek to gauge discrimination against groups defined by race, gender, and other protected attributes. A common strategy is to estimate disparities after controlling for observed covariates in a regression model. However, not all relevant factors may be available to researchers, leading to omitted variable bias. Conversely, controlling for all available factors may also skew results, leading to so-called "included variable bias". We introduce a simple strategy, which we call risk-adjusted regression, that addresses both concerns in settings where decision makers have clear and measurable policy objectives. First, we use all available covariates to estimate the expected utility of possible decisions. Second, we measure disparities after controlling for these utility estimates alone, omitting other factors. Finally, we examine the sensitivity of results to unmeasured confounding. We demonstrate this method on a detailed dataset of 2.2 million police stops of pedestrians in New York City.

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