## Description

Back to topRandom 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## Speakers

Back to top## Schedule

Back to top**Speaker: **Holger Dette (Ruhr-Universität Bochum)

**Speaker: **Sam Pimentel (University of California, Berkeley)

**Speaker: **Lihua Lei (Stanford University)

**Speaker: **Jingshen Wang (University of California, Berkeley)

**Speaker: **Anqi Zhao (National University of Singapore)

**Speaker: **Tirthankar Dasgupta (Rutgers University)

**Speaker: **Mona Azadkia (ETH Zürich and London School of Economics)

**Speaker: **EunYi Chung (University of Illinois at Urbana-Champaign)

**Speaker: **Adrian Roellin (National University of Singapore)

**Speaker: **Panos Toulis (University of Chicago)

**Speaker: **Bodhi Sen (Columbia University)

**Speaker: **Philip Stark (University of California, Berkeley)

**Speaker: **Colin Fogarty (University of Michigan)

**Speaker: **Lei Shi (University of California, Berkeley)

**Speaker: **Nianqiao Phyllis Ju (Purdue University)

**Speaker: **Lester Mackey (Microsoft New England)

**Speaker: **Jingshu Wang (University of Chicago)

**Speaker: **Yaniv Romano (Technion – Israel Institute of Technology)

**Speaker: **Xinran LI (University of Illinois at Urbana-Champaign)

## Videos

Back to topCovariate-adaptive randomization inference conditional on optimal propensity score matching

Sam Pimentel

August 22, 2023

When Is A Randomization Test for Spillover Effects Also A Permutation Test?

Panos Toulis

August 23, 2023

Measuring association on topological spaces using kernels and geometric graphs

Bodhi Sen

August 24, 2023

Exact and Conservative Inference in Blocked Experiments with Binary Outcomes

Philip Stark

August 24, 2023

Unifying Modes of Inference for Average Treatment Effects in Randomized Experiments

Colin Fogarty

August 24, 2023

Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes

Lei Shi

August 24, 2023

A simple Markov chain for independent Bernoulli variables conditioned on their sum

Nianqiao Phyllis Ju

August 24, 2023