Carlos Cinelli

About

I am an assistant professor at the Department of Statistics at the University of Washington. I am also a data science fellow in the eScience Institute, and the Consulting Director of the Center for Statistics and the Social Sciences. I obtained my Ph.D. in Statistics at the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl.

My research focuses on developing new causal and statistical methods for transparent and robust causal claims in the empirical sciences. I am 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.

News

Submissions for the 2024 American Causal Inference Conference are now open.
Consider attending the 2023 Interactive Causal Learning Conference in Boca Raton, Florida.
New working paper, Causally Sound Priors for Binary Experiments (with Nicholas J. Irons).
Consider attending our short course on Causal Directed Acyclic Graphs at the APSA 2023.
New working paper on NBER, Long Story Short: Omitted Variable Bias in Causal Machine Learning (with V. Chernozhukov, W. K. Newey, A. Sharma, and V. Syrgkanis).
The videos of the 2022 Interactive Causal Learning Conference are now available online.
Paper accepted in Colt 2022, Orthogonal Statistical Learning with Self-Concordant Loss (with Lang Liu and Zaid Harchaoui).
Paper accepted in Sociological Methods and Research, A Crash Course in Good and Bad Controls (with Andrew Forney and Judea Pearl).
Paper accepted in Nature Communications, Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy (with N. LaPierre, B. Hill, S. Sankararaman, and E. Eskin).
New sensitivity analysis package sensemakr for Python (PySensemakr) is now available for testing.
I'm joining the Editorial Board of the Journal of Causal Inference (JCI). Consider submitting your work.
The recording of my talk for the Online Causal Inference Seminar is now available on YouTube.
I am actively looking for students with strong interests in causal inference. Please feel free to reach out!
Other researchers and I talked to BBC Brazil about the (mis)use of Benford's Law for fraud detection in elections (in Portuguese).
Watch our video recorded for JSM 2021, Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy.
I will join the Department of Statistics at the University of Washington as an assistant professor starting this Fall 2021.
Paper accepted in AISTATS 2021, Exploiting Equality Constraints in Causal Inference (with Chi Zhang, Bryant Chen and Judea Pearl).
New preprint on bioRxiv, Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy.
Paper accepted in European Journal of Epidemiology, Generalizing Experimental Results by Leveraging Knowledge of Mechanisms (with Judea Pearl).
Watch our video recorded for JSM 2020, An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables.
Watch our video recorded for useR! 2020 sensemakr: Sensitivity Analysis Tools for OLS.
Paper accepted in ICML-2020, Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets (with Daniel Kumor and Elias Bareinboim).
New software paper, sensemakr: Sensitivity Analysis Tools for OLS in R and Stata (with Jeremy Ferwerda and Chad Hazlett).
New technical report, A Crash Course in Good and Bad Controls (with Andrew Forney and Judea Pearl).
Paper forthcoming in JRSS-B, Making Sense of Sensitivity: Extending Omitted Variable Bias (with Chad Hazlett).
New working paper on the generalizability of experimental findings (with Judea Pearl).
New tutorial on Generalizability in Causal Inference presented at the Southern California Methods Conference (with Elias Bareinboim).
Paper accepted in ICML-2019, Sensitivity Analysis of Linear Structural Causal Models.