Tamer Cetin
Causal Inference -- Machine Learning -- AI
Causal Inference -- Machine Learning -- AI
Tamer Çetin is a Research Professor at Stanford whose work focuses on applied econometrics, causal inference, and machine learning. His research develops robust methods for statistically reliable empirical analysis in high-dimensional and complex-data settings, with contributions spanning weak identification, instrumental variables, regression discontinuity, debiased machine learning, and causal estimation.
At Stanford, he connects modern statistical learning methods with classical questions in identification, inference, and policy evaluation. His research seeks to improve the credibility of empirical conclusions when researchers face complex data, competing identification strategies, imperfect instruments, or model uncertainty.
Before joining Stanford, Dr. Çetin held research and teaching positions across academia, consulting, and industry. He has taught courses in economics, econometrics, and data science. His broader research interests include causal inference, health economics, and the use of machine learning in empirical research.
Stanford University
Palo Alto, CA
Email: tcetin@stanford.edu