2026
Statistics Colloquium: Peter McCullagh
11:30 am–12:30 pm Jones 303
Peter McCullagh
Department of Statistics
University of Chicago
Title: Causal Inference Versus Stochastic Models
Abstract: The classical statistical model is a stochastic process, which means a probability distribution on the space of outcomes. Sampling distributions and distributional properties of derived summaries follow from the process. Logical positivism in the sense of von Mises reverses the natural order, beginning with an outcome variable or kollektiv, from which probabilities are defined as limiting frequencies. Causal inference is based on the same frequency definition supplemented by random sampling. These notions of probability are in conflict, even when limiting frequencies exist. Accordingly, all derived concepts such as expectation, independence, random variable, and treatment effect are also in conflict. Broadly speaking, the frequentist concept is adequate for sample surveys and other areas of application such as auditing, where the population is concrete, finite, and accessible for simple random sampling. These conditions are not met in typical scientific research work, where the population is abstract, infinite and only partially accessible. The emphasis in this talk is on stochastic regression models, where random sampling does not arise, treatment assignment is associated with a Markov kernel, and the treatment effect is a group action on the parameter space.
Statistics Colloquium: Kaizheng Wang
11:30 am–12:30 pm Jones 303
Kaizheng Wang
Department of Industrial Engineering and Operations Research/Data Science Institute
Columbia University
Title: Learning to Augment Statistical Inference with Generative Models
Abstract: Generative models can produce seemingly unlimited synthetic data, yet discrepancies between synthetic and real populations can introduce bias and undermine statistical conclusions. For a new task with scarce or no real data, how much synthetic data can be safely used? This talk develops a general framework that leverages historical tasks to calibrate synthetic-data augmentation and quantify the resulting uncertainty. When no real observations are available, the framework adaptively selects the synthetic sample size to achieve nominal average-case coverage. In the application to LLM-generated survey responses, this calibrated size can be interpreted as the number of human respondents the LLM is effectively worth, providing a measure of its simulation fidelity. More generally, when real observations are available, the augmentation scheme is characterized jointly by the number of synthetic observations and the weight assigned to each. We learn a size–weight frontier that identifies the largest synthetic contribution compatible with reliable uncertainty quantification. Together, these results show how generative models can strengthen statistical analysis when real data are limited.
DSI/Statistics Colloquium: Frederic Koehler
11:30 am–12:30 pm DSI 105
Frederic Koehler
DSI/Department of Statistics
University of Chicago
Title: A Least-Squares Perspective on Ellipsoid Fitting
Abstract: Suppose we have n i.i.d. random vectors X_1,\ldots, X_n in d dimensions. How large can n be, as a function of d, such that there exists an ellipsoid interpolating all n points? Saunderson, Parrilo, and Willsky conjectured an explicit answer (n ~ d^2/4) for Gaussian data. Subsequently, many authors made rigorous progress on this question. We study a natural least-squares generalization of ellipsoid fitting, over a general class of data distributions, and solve its high-dimensional limit—- this proves the Gaussian conjecture as a special case. The solution combines in a nice way ideas from high-dimensional probability, statistical learning, and convex geometry + optimization.
Based on a joint work with Youngtak Sohn.
DSI/Statistics Colloquium: Nikolaos Ignatiadis
11:30 am–12:30 pm DSI 105
Nikolaos Ignatiadis
DSI/Department of Statistics
University of Chicago
Title: Borrowing Strength in Multiple Testing with Compound p-values
Abstract: Many large-scale multiple testing problems in high-throughput biology involve thousands of units (e.g., genes or proteins) but only a small number of replicates per unit. A key observation in the microarray literature around the turn of the century was that standard t-tests may be nearly powerless in this setting. This motivated the development of methods such as the B-statistic (Lönnstedt and Speed, 2002), Significance Analysis of Microarrays (SAM; Tusher, Tibshirani and Chu, 2001), and limma (Smyth, 2004), which borrow information across units.
We study this type of borrowing across units through the lens of compound p-values. Unlike bona fide p-values, compound p-values need not be valid for each hypothesis separately, but instead satisfy an average superuniformity property across the null hypotheses. We first explain how compound p-values arise both from a nonparametric version of limma and from SAM. We then use the compound p-value perspective to construct a procedure that achieves the asymptotic power of the oracle Bayes rule in a sparse regime under the hierarchical model underlying the B-statistic and provides finite-sample frequentist control of the false discovery rate without requiring this hierarchical model to hold.
Statistics Colloquium: Xinran Li
11:30 am–12:30 pm Jones 303
Xinran Li
Department of Statistics
University of Chicago
Title: Randomization Inference with Sample Attrition
Abstract: Sample attrition, or equivalently missing outcomes, is a common challenge in randomized controlled experiments, and analyses that simply discard units with missing outcomes may suffer from severe bias. In this talk, we develop computationally efficient methods for randomization inference that remain valid under a broad class of potentially informative missingness mechanisms, allowing a unit’s missingness to depend on its potential outcomes. We pay particular attention to monotone missingness mechanisms, under which outcomes are systematically more likely to be observed under treatment than under control, or vice versa. Such restrictions arise naturally in many empirical settings, especially in the social sciences.
Specifically, we construct valid p-values for testing sharp, bounded, and quantile null hypotheses on treatment effects through a worst-case consideration of the classical Fisher randomization test. By leveraging distribution-free test statistics, these worst-case p-values can be computed efficiently and often admit closed-form solutions. Importantly, by incorporating both potential outcomes and potential missingness indicators into the test statistic, our methods can exploit structural assumptions such as monotone missingness to sharpen inference. Our approach also connects to a range of partial-identification bounds in the literature. We illustrate the proposed methods through simulation studies and empirical applications evaluating the impacts of job training, media censorship, and artificial intelligence.
Student Seminar: Yinjie Wang
4:30–5:00 pm Jones 111
Thursday, July 23, 2026, at 4:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yinjie Wang, Department of Statistics, The University of Chicago
“Simple and Effective Query-Adaptive Sparse Attention”
Student Seminar: Arnav Rastogi
2:00–2:30 pm Jones 111
Thursday, July 23, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Arnav Rastogi, Department of Statistics, The University of Chicago
“Measuring, Representing, and Steering Premium–Value Brand Positioning in Beauty and Personal Care”
Student Seminar: Brian Park
10:00–10:30 am Jones 111
Wednesday, July 22, 2026, at 10:00 AM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Brian Park, Department of Statistics, The University of Chicago
“Separating Predictive Gains from Incremental Information: Text Embeddings and Analyst Forecasts of Corporate Earnings”
Student Seminar: Yuta Iwai
2:30–3:00 pm Jones 111
Monday, July 20, 2026, at 2:30 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Yuta Iwai, Department of Statistics, The University of Chicago
“Evaluation of McScan for Change Point Detection in High-Dimensional Linear Regression with an Application to Macroeconomic Data”
Student Seminar: Pratik Chheda
2:00–2:30 pm Jones 111
Monday, July 20, 2026, at 2:00 PM, in Jones 111, 5747 S. Ellis Avenue
Master’s Thesis Presentation
Pratik Chheda, Department of Statistics, The University of Chicago
“Comparing Multivariate Value at Risk Forecasting Models: A Reproduction and Empirical Extension”