We are pleased to announce that Carlos Cinelli, Assistant Professor of Statistics, has received grant funding support from the National Science Foundation (NSF) for his proposal “Sensitivity Analysis Tools for Quasi-Experimental Designs”.
Applied causal inference work in the social, behavioral, and economic sciences often use a handful of methods commonly referred to as "quasi-experimental" designs. However, the validity of these methods requires strong assumptions about the data-generating process, many of which are difficult to defend in practice. What if these assumptions are false? In such cases, sensitivity analyses play an essential role by allowing researchers to quantify how strong violations of key assumptions need to be in order to substantially change a research conclusion. This project develops new theory, methods and software for assessing the sensitivity of causal inferences to violations of underlying assumptions when using such methods. These results allow applied scientists and policymakers to draw more robust and trustworthy conclusions using valid, yet imperfect scientific evidence.
Congratulations to Carlos on his success!
Cinelli Receives NSF Award
