Robust Causal Discovery Professors in Global

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Daniel Malinsky
Columbia University United States of America
causal discovery
causal inference
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David Inouye
Purdue University United States of America
robust machine learning
causal machine learning
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Phebe Vayanos
University of Southern California United States of America
robust optimization
causal inference
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Phebe Vayanos
University of Southern California United States of America
robust optimization
causal inference
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Nathan Kallus
Cornell University Netherlands
robust optimization
causal inference
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Phebe Vayanos
University of Southern California United States of America
robust optimization
causal inference
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Cem Dalli
Aarhus University Denmark
causal machine learning
causal inference
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Anish Agarwal
Columbia University United States of America
causal machine learning
causal inference
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Dingke Tang
University of Ottawa Canada
robust statistics
causal inference
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Zhijing Jin
University of Toronto Canada
causal inference
causal reasoning
adversarial robustness
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Dae Ham
University of Michigan United States of America
causal inference
design-based causal inference
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Jason Hartford
University of Manchester United Kingdom
causal inference
causal representation learning
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Bryon Aragam
University of Chicago Booth School of Business United States of America
causal machine learning
causality
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Vanessa Mcnealis
University of Glasgow United Kingdom
causal inference
robustness
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Sebastian Denzler
Friedrich-Alexander-Universität Erlangen-Nürnberg Germany
robust bayesian optimization
distributionally robust optimization
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Hans Heyn
University of Gothenburg Sweden
causal analysis
causality
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Hans Heyn
Chalmers University of Technology Sweden
causal reasoning
causality
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Tobias Eifler
Technical University of Denmark Denmark
robust design
structural robustness
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Fabrizio Russo
Imperial College London United Kingdom
causal discovery
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Francisco Santos
Technical University of Denmark Denmark
causal discovery
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Tom Heskes
Radboud University Netherlands
causal discovery
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