Announcements

Please join us for an exciting day of networking and continuing education events in Kansas State University. The Kansas-Western Missouri Chapter of ASA and the Kansas State University Department of Statistics are co-sponsoring a Short Course, and the annual Spring Chapter Meeting will be held at its conclusion. The short course and keynote address will be conducted by Dr. Jiwei Zhao from the University of Wisconsin-Madison.

Short Course: Introduction to Causal Inference from a Semiparametric Lens

Presenters: Jiwei Zhao, Department of Statistics, University of Wisconsin-Madison Date: May 1^st, 2026, 9:00 am to 12:00 pm, 1:00 pm to 2:00 pm

Location: Tallgrass Ballroom, Kramer Dining Center, 104 Pittman Building, 1835 Claflin Rd, Manhattan, KS, 66502

Abstract: Why does a treatment work? Does a policy actually improve outcomes? Answering these questions requires moving beyond correlation to causation and doing so with statistical rigor. This short course provides an accessible introduction to causal inference and the semiparametric ideas that power modern causal analysis, culminating in the increasingly popular framework of double machine learning. The course is designed for statisticians, data scientists, and quantitative researchers who want to understand how causal effects can be estimated reliably, especially when flexible machine learning methods are used to handle complex data. No prior background in causal inference or semiparametric theory is assumed, but some basic knowledge of mathematical statistics would be of help.

Dr. Jiwei Zhao is currently an Associate Professor at the University of Wisconsin-Madison. His research interests include semiparametric statistics, the tradeoff between efficiency and robustness, domain adaptation and transfer learning, missing data analysis and causal inference. He also conducts research on developing trustworthy statistical inference methods for AI-predicted data and, more broadly, for synthetic data, with a focus on reliability, robustness, and

principled uncertainty quantification. His work has been published in top-tier statistical journals as well as in leading machine learning conferences. His research has been consistently supported by the US National Science Foundation and the National Institutes of Health. Jiwei is now Associate/Action Editor for journals in both statistics and machine learning, such as, Annals of Applied Statistics, JRSS Series A: Statistics in Society, and Transactions on Machine Learning Research (TMLR). He also serves as Area Chair for NeurIPS, ICML and AISTATS.

Kansas-Western Missouri Chapter Meeting

Date: May1^st, 2026, 2:30 pm to 3:30 pm.

Location: Tallgrass Ballroom, Kramer Dining Center, 104 Pittman Building, 1835 Claflin Rd, Manhattan, KS, 66502

Agenda: 2:30 pm - 2:45 pm Chapter Business

2:45 pm - 3:30 pm Keynote Speech

Keynote Title: Harnessing AI-Predicted Data for Trustworthy Statistical Inference

Abstract: The rapid advancement of AI/ML has made it increasingly feasible to generate predicted data at scale using black-box models, including deep learning, large language models, and generative AI. These predictions offer a promising way to augment scarce gold-standard data and boost the power of downstream scientific analyses. However, naively substituting predictions for true outcomes in statistical inference can introduce bias and inflate false positive rates, undermining the trustworthiness of scientific conclusions. In this talk, I will present a series of methods for safely and efficiently integrating AI-predicted data into rigorous statistical inference. I will first introduce an assumption-lean and data-adaptive framework that guarantees valid inference without assumptions on the ML model while adaptively leveraging accurate predictions for efficiency gains. I will then extend these ideas to settings with covariate shift arising from automated computational phenotypes in electronic health records, and to SADA, a method for safely aggregating multiple black-box predictions of uncertain quality. Throughout, I will highlight the semiparametric efficiency theory underpinning these methods and demonstrate their practical impact through applications in genomics and precision medicine.

Online Registration Links:

Short Course: ASA Traveling Short Course Spring 2026

Chapter Meeting and Lunch: ASA Chapter Meeting Spring 2026