Distinguished Speaker Series

Traditional statistical methods need to be updated when we move beyond simple models of random sampling, constant effects, and accurate measurements. In this seminar, we consider general challenges of design and analysis in a world of nonrandom samples, varying treatment…

We will discuss how to detect and ameliorate model dependence, where small, indefensible changes in model specification have large, perhaps unintended impacts on our conclusions. Easy-to-use matching methods for both observational and experimental data will be discussed.

This lecture focuses on linear models of mediation, in which both the mediator and the outcome variable are measured at the interval level. Considerable attention is devoted to dealing with confounding and measurement error in mediational analyses, focusing on design,…

This seminar is an introduction to econometric models for such discrete choices. We will discuss several types of discrete choice models for describing individual choice behavior.

This seminar will compare and contrast the benefits and pitfalls of using a regression-based approach to mediation, moderation, and conditional process analysis compared to an SEM approach.

Meta-analysis is the branch of statistics that deals with the analysis of data from several studies investigating the same question. This seminar is an introduction to methods of meta-analysis.

This seminar will explore detailed ordinal regression case studies using R, and will introduce the Bayesian Wilcoxon test through a Bayesian PO model.

This seminar will describe a framework for causal inference from real world data.

This seminar introduces new skills for analyzing causal models by connecting statistical models to scientific models of causal processes.

In this course, we will discuss recent developments that enable valid inference with “double dipping”, or when an analyst explores existing data to come up with a statistical question and then uses the same data to answer that question.

This seminar will provide an overview of the principles of causal inference and the extension of those principles to outcome-wide studies.