Xinran Li

Assistant Professor

University of Chicago

United States

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About

Dr. Xinran Li is an Assistant Professor at the University of Chicago, USA. His research primarily focuses on statistical methodologies related to randomization inference, treatment effects, and the design and analysis of randomized experiments. Recent publications include works on rerandomization techniques, sensitivity analysis in observational studies, and statistical evaluations of treatment effects in clinical trials.

Articles (15)

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2025

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2021

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2021

Collaborators (3)

Bo Zhang

Assistant Professor of Biostatistics

UNITED STATES

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Peng Ding

University of California

UNITED STATES

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Jun Liu

Professor in Statistics

Harvard University

UNITED STATES

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Xinran Li

Research Interests

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About

Articles (15)

Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance

Randomization-based Z-estimation for evaluating average and individual treatment effects

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials

Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

Some theoretical foundations for the design and analysis of randomized experiments

Kernel-Based Partial Permutation Test for Detecting Heterogeneous Functional Relationship

Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects

Treatment effect quantiles in stratified randomized experiments and matched observational studies

Power and sample size calculations for rerandomization

Randomization-Based Test for Censored Outcomes: A New Look at the Logrank Test

Rerandomization with diminishing covariate imbalance and diverging number of covariates

Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers

Rejective Sampling, Rerandomization, and Regression Adjustment in Survey Experiments

On the optimality of sliced inverse regression in high dimensions

A Multi-resolution Theory for Approximating Infinite-<i>p</i>-Zero-<i>n</i>: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff

Collaborators (3)

Bo Zhang

Peng Ding

Jun Liu

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