Uncovering cause and effect relationships.

Causal inference is a fundamental branch of statistics that provides rigorous methods for determining cause-and-effect relationships from data. Unlike traditional statistical approaches that focus on correlation, causal inference aims to answer "what if" questions and understand how interventions affect outcomes.

Faculty exploring causal inference. 

Color portrait of woman smiling at camera, long blonde hair, glasses
Jelena Bradic
Professor of Statistics and Data Science
Jelena Bradic
Professor of Statistics and Data Science
Contact
jelena.bradic<at>cornell<dot>edu
Bradic's Website
A photo of Sainyam Galhotra, a man with a shaved head, a dark beard, dark glasses and a blue shirt in front of a leafy background.
Sainyam Galhotra
Assistant Professor of Computer Science
Sainyam Galhotra
Assistant Professor of Computer Science
Contact
sg@cs.cornell.edu
Galhotra's Website
color portrait of man looking at the camera
Yang Ning
Associate Professor of Statistics and Data Science, Director of Graduate Studies, Statistics and Data Science
Yang Ning
Associate Professor of Statistics and Data Science, Director of Graduate Studies, Statistics and Data Science
Contact
(607) 255-3759
yn265@cornell.edu
Ning's Website
A color photo of David Ruppert in front of a gray background
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
Contact
dr24@cornell.edu
Ruppert's Website