Carlos Cinelli

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington.12 He is also a data science fellow at the eScience Institute and an affiliate faculty member of the Center for Statistics and the Social Sciences.23

Research Focus

Cinelli's research primarily focuses on developing new theory, methods, and software to help data scientists make reliable causal inferences under realistic settings.1 His work aims to:

1. Enable empirical scientists to assess the robustness of their findings against plausible violations of traditional assumptions
2. Address inferential challenges faced by social and health scientists
3. Explore the intersection of causality with machine learning and artificial intelligence

His research has been published in prestigious outlets in statistics, epidemiology, and machine learning, and has had a significant impact on research practices across various disciplines, including economics, political science, neurodevelopment, epidemiology, and genetics.1

Education and Background

Cinelli obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl.2

Current Projects

At the University of Washington, Cinelli is working on two main projects:

1. Developing theory and software to make sensitivity analysis a routine and standard practice across empirical sciences
2. Establishing foundations for a more flexible approach to causal inference with credible assumptions1

Cinelli's work aims to create more transparent and robust causal claims in empirical sciences, with a particular focus on the challenges faced by social and health scientists.24