معرفی
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl.
His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include:
- Causal inference challenges in social and health sciences
- Intersections of causality with machine learning and AI
- Sensitivity analysis for omitted variable bias
- Robust statistical methods for observational studies
- Instrumental variables and Mendelian randomization
- Generalizability of experimental findings
His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics.
Honors include:
- Best Paper Award at SBE 2024 in Econometrics
- UCLA Dissertation Year Fellowship (2020)
He actively advises PhD students and has received research funding from:
- NSF/MMS
- Royalty Research Fund
