Permutation and Causal Inference

Description

Back to top

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.

Organizers

Back to top

R B

P D

F H

N P

Speakers

Back to top

M A

E Y C

T D

H D

C F

N J

L L

X L

L M

S P

A R

Y R

B S

L S

P B S

P T

J W

J W

A Z

Registration

Back to top

IMSI is committed to making all of our programs and events inclusive and accessible. Contact to request accommodations.

In order to register for this workshop, you must have an IMSI account and be logged in. Please use one of the buttons below to login or create an account.