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Random permutation, as a particularly interesting type of stochasticity, has been a fundamental
object of interest in two branches of statistics: causal inference, which focuses on drawing
causal conclusions from randomized and quasi-randomized experiments, and distribution-free
methods, which focuses on constructing and studying the stochastic structures of certain
functionals of a distribution-free nature. The two fields have each witnessed explosive
development in recent years. Notably, as the ideas of randomization, re-randomization, and
multiple permutation tests have been booming in causal inference in the last ten years,
conformal prediction, knockoffs, rank statistics, graph-based statistics, optimal transport,
combinatorial inference, and Stein’s methods have simultaneously received increasing attention
in the world of distribution-free methods.

Researchers working in these two areas are now, more than ever, realizing the foundational
connection between them: they are faced with similar data analysis challenges and need similar
technical tools. This workshop will bring experts from these two distinct worlds together, to communicate, to learn from each other, and to stimulate conversations and collaborations.


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Rina Barber University of Chicago
Peng Ding University of California, Berkeley
Fang Han University of Washington
Nicole Pashley Rutgers University