Causal Inference | Cornell Bowers

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.

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Professor of Statistics and Data Science

jelena.bradic<at>cornell<dot>edu

Jelena Bradic

Office:

Computing and Information Science Building 303

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

sg@cs.cornell.edu

Sainyam Galhotra

Office:

Gates Hall 445

Galhotra's Website

color portrait of man looking at the camera

Associate Professor of Statistics and Data Science, Director of Graduate Studies, Statistics and Data Science

yn265@cornell.edu

Yang Ning

Office:

Computing and Information Science Building 306

Phone:(607) 255-3759

A color photo of David Ruppert in front of a gray background

Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science

dr24@cornell.edu

David Ruppert

Office:

Computing and Information Science Building 307

Ruppert's Website

Continue Exploring

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research office

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