QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: Panel data are commonly used in the social sciences to study the causal effects of policy interventions on certain outcomes. Yet the assumptions under which popular panel data methods—such as difference-in-differences and two-way fixed effect models—can perform properly are often unsatisfied. We discuss an emerging literature on causal inference with panel data and focus on a special setting of a single, dichotomous treatment variable. Specifically, we introduce both a parametric approach (the generalized synthetic control method) and a non-parametric approach (trajectory balancing) to deal with the failure of the “parallel trends” assumption and provide recommendations for applied researchers.
Yiqing Xu is an assistant professor of political science at UC San Diego, studying political methodology and Chinese politics. He received his doctorate in political science from Massachusetts Institute of Technology in 2016, a master’s degree in economics from Peking University in 2010 and a bachelor’s degree in economics in 2007 from Fudan University. His work has appeared in American Political Science Review, American Journal of Political Science, The Journal of Politics, Political Analysis, among other peer-reviewed journals. He has won the American Journal of Political Science Best Paper Award for 2016 and the Miller Prize for the best work appearing in Political Analysis in 2017, among a few other professional awards.
This workshop series is being sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.