“Heterogeneous Causal Effects: A Propensity Score Approach “ Abstract: Heterogeneity is ubiquitous in social science. Individuals differ not only in background characteristics, but also in how they respond to a particular […]
The UCLA Department of Statistics and the Center for Social Statistics presents: Rules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government Collaboration between statistical scientists […]
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 […]
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 […]
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 […]
Title: 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 […]
Title: “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 […]
Title: How to Make Causal Inferences Using Texts Abstract: 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 […]
Title: How to Make Causal Inferences Using Texts Abstract: 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 […]
Summer Institute in Computational Social Science Panel Presentation Luskin Conference Center Laureate Room 2:00pm – 3:15pm Digital Demography Prof. Dennis Feehan, UC Berkeley and Prof. Ka-Yuet Liu, UCLA 3:30pm – […]
