TU3.R4.3

Neyman-Pearson Causal Inference

Joni Shaska, Urbashi Mitra, University of Southern California, United States
Session:
Hypothesis Testing 2
Track:
11: Information Theory and Statistics
Location:
Omikron II
Presentation Time:
Tue, 9 Jul, 15:05 - 15:25
Session Chair:
Yuval Kochman, Hebrew University
Presentation
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Discussion
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Session TU3.R4
TU3.R4.1: Detection of Correlated Random Vectors
Dor Elimelech, Ben-Gurion University of the Negev, Israel; Wasim Huleihel, Tel Aviv University, Israel
TU3.R4.2: Testing Dependency of Weighted Random Graphs
Mor Oren-Loberman, Vered Paslev, Wasim Huleihel, Tel Aviv University, Israel
TU3.R4.3: Neyman-Pearson Causal Inference
Joni Shaska, Urbashi Mitra, University of Southern California, United States
TU3.R4.4: Large Deviations for Statistical Sequence Matching
Lin Zhou, Qianyun Wang, Jingjing Wang, Lin Bai, Beihang University, China; Alfred Hero, University of Michigan, Ann Arbor, United States
Resources
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