Carnegie Mellon University

Causal Inference

Causal inference is increasingly being recognized as a crucial part of science and society. Indeed, understanding cause-effect relationships – rather than mere associations – is the primary goal in many, if not most, scientific fields. Causal inference is a broad discipline, intersecting with not only statistics and machine learning, but medicine, philosophy, public policy, and much more. Important contributions can be made in applications, methods, theory, and everywhere in between.

The Causal Inference Working Group at CMU started in 2016. We meet weekly to discuss our own research or interesting papers, both new and old; members come from communities in Statistics & Data Science, Machine Learning, Information Systems & Public Policy, Philosophy, Epidemiology, and beyond.

Faculty with this Research Interest

11 bios displayed.

Siva Balakrishnan

Siva Balakrishnan

Associate Professor (on leave Spring 2025)

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Eli Ben-Michael

Eli Ben-Michael

Assistant Professor, joint with Heinz College

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Zach Branson

Zach Branson

Assistant Teaching Professor and Assistant Director for the Undergraduate Program

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David Choi

David Choi

Associate Professor of Statistics and Information Systems

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Amelia Haviland

Amelia Haviland

Anna Loomis McCandless Professorship of Statistics & Public Policy

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Edward Kennedy

Edward Kennedy

Associate Professor

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Kathryn  Roeder

Kathryn Roeder

UPMC Professor of Statistics and Life Sciences

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Cosma Shalizi

Cosma Shalizi

Associate Professor

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Yandi Shen

Yandi Shen

Assistant Professor

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Larry Wasserman

Larry Wasserman

UPMC University Professor (on leave Spring 2025)

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