Carlos Cinelli

Session: Short Course 2
Title: Sensitivity Analysis for Causal Inference in the Social Sciences
Abstract: The past few decades have witnessed rapid and unprecedented theoretical progress on the science of causal inference. Most of this theoretical progress, however, relies on strong, exact assumptions, such as the absence of unobserved common causes (ignorability assumptions), or the absence of certain direct effects (exclusion restrictions). Unfortunately, more often than not these assumptions are very hard to defend in practice, especially in the social sciences. This leads to two undesirable consequences for applied quantitative work: (i) important research questions may be neglected, simply because they do not exactly match the requirements of current methods; or, (ii) researchers may succumb to making the required “identification assumptions” simply to justify the use of available methods, but not because these assumptions are truly believed (or understood).  In this course, we will cover new theory, methods, and software for permitting causal inferences under more flexible and realistic settings. We will focus on a flexible suite of sensitivity analysis tools for common identification strategies, such as confounding adjustment, instrumental variables, and difference-in-differences. These tools can be immediately put to use to improve the robustness and transparency of current applied research, and students are encouraged to bring their own examples. 

Carlos is an assistant professor at the Department of Statistics at the University of Washington. He is also a data science fellow in the eScience Institute, and the Consulting Director of the Center for Statistics and the Social Sciences. He obtained my Ph.D. in Statistics at the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in the empirical sciences. He is particularly interested in the inferential challenges faced by social and health scientists, as well as the intersections of causality with machine learning and artificial intelligence. Carlos Cinelli's website.