Adeline Lo, Princeton University Abstract: High dimensional (HD) data, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection […]
Lan Liu, University of Minnesota at Twin Cities “Parsimonious Regressions for Repeated Measure Analysis” Abstract: Longitudinal data with repeated measures frequently arises in various disciplines. The standard methods typically impose a mean outcome model as a function of individual features, time and their interactions. However, the validity of the estimators relies on the correct specifications […]
Eloise Kaizar, Ohio State University Randomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants, trial results may not directly reflect the average treatment effect that would follow real world adoption […]
Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies
Abstract: This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies, including settings with unconfoundedness or instrumental variables. Multi-armed bandits for learning treatment assignment policies will also be considered.
How to Make Causal Inferences Using Texts
Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference, and provide solutions to the problems we raise. We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. Estimating this latent representation, however, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects. To address these risks, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness. Our work provides a rigorous foundation for text-based causal inferences, connecting two previously disparate literatures. (Joint Work with Egami, Fong, Grimmer and Roberts)
Prediction in Social Science: A Tool to Study Inequality in Populations Biography: Ian Lundberg is a Postdoctoral Scholar in the Department of Sociology and California Center for Population Research at UCLA. His research develops statistical and machine learning methods to answer new questions about inequality in America. Past work is published or forthcoming in PNAS, the American […]
In this workshop, attendees will learn how to analyze survey data while accounting for its complex survey design. We will demonstrate how to specify the survey design, impute any missing data, and analyze the survey outcomes of interest. We will discuss how our downstream “analysis” steps are related to initial operational “design” choices made by […]
Biography: Jack Mountjoy is an Assistant Professor of Economics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research explores the economics and econometrics of education, labor markets, and social mobility. Prior to joining Chicago Booth, he was a Postdoctoral Fellow in Economics at Princeton University in the Industrial […]
Biography: Graeme Blair is an associate professor of political science at UCLA and serves as Co-Director of Training and Methods of Evidence in Governance and Politics (EGAP). Graeme uses experiments, field research, and statistics to study how to reduce violence and how to improve social science research. He works primarily in Nigeria, often in partnership with […]
Workshop: Preproducibility: what we may not, with advantage, omit Please note that there will be no remote attendance for this event. All attendees must attend the workshop in person. Panelists: Philip B. Stark (Remote), Yotam Shem-Tov, Irene Bloemraad (Remote), and Randall Kuhn Moderator: Patrick Heuveline Presenter: Philip B. Stark is Distinguished Professor of Statistics at the University of […]