Title: Covariate Selection for Generalizing Experimental Results Abstract: Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity. Although this separating set […]
CSS Event
Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data Abstract: Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines, matching methods are rarely used when analyzing time-series […]
Title: Matching Methods for Causal Inference with Time-Series Cross-Section Data Abstract: Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines, matching methods are rarely used when analyzing time-series […]
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)
Biography: Gary Solon is Professor Emeritus of Economics at the University of Michigan. He was Eller Professor of Economics at the University of Arizona during 2015-2018 and Professor of Economics at Michigan State University during 2007-2015. He is a research associate at the National Bureau of Economic Research, a fellow of the Society of Labor […]
Instructor: Neal Fultz This workshop has two halves. In the first half, we will dive into the 3 main computing resources that CCPR offers to affiliates, including it’s remote and on campus offerings. At the end of the first half, we’ll get participants signed up for hoffman2 and TS2. Once signed up, you’ll have state […]
Biography: Christopher Walters is an Associate Professor of Economics in the Department of Economics at the University of California, Berkeley. Dr. Walters joined the faculty at Berkeley after completing his PhD in economics at MIT in 2013. He is also a Research Associate in the NBER programs on education and labor studies, an IZA Research […]
Biography: Peter Hull is a Professor of Economics at Brown University, a Faculty Research Fellow in the NBER Labor Studies, Education, and Health Care programs in Labor Studies, and the econometrics editor at the Review of Economics and Statistics. His research spans a variety of topics in applied econometrics, education, health care, discrimination, and criminal […]