These days I like to discuss
- Analytic Design Theory
- Statistical Communication
- The Casual Inference Podcast
- Large-scale medical data
- Italian
- Co-founding R-Ladies Nashville
- Disney World
over coffee

Lucy D’Agostino McGowan
Lucy D’Agostino McGowan is an assistant professor in the Department of Statistical Sciences at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on causal inference, statistical communication, analytic design theory, and data science pedagogy. Dr. D’Agostino McGowan was the 2023 chair of the American Statistical Association’s Section on Statistical Graphics and can be found blogging at livefreeordichotomize.com, on Twitter @LucyStats, and podcasting on the American Journal of Epidemiology partner podcast, Casual Inference.
Recent Awards
- In 2025, Lucy received the Emerging Leader Award from the Committee of Presidents of Statistical Societies
- In 2023, Lucy was selected for the Teaching in the Health Sciences Young Investigator Award for her paper Design Principles for Data Analysis
- In 2023, Lucy was selected as an ASA StatsForward Fellow
Listen to the Casual Inference Podcast
Recent & Upcoming Talks
The Why Behind Including Y in your Imputation Model

Handling missing data is a frequent challenge in analyses of health data, and imputation techniques are often employed to address this issue. This talk focuses on scenarios where a covariate with missing values is to be imputed and examines the prevailing recommendation to include the outcome variable in the imputation model. Specifically, we delve into stochastic imputation methods and their effects on accurately estimating the relationship between the imputed covariate and the outcome. Through mathematical proofs and a series of simulations, we demonstrate that incorporating the outcome variable in imputation models is essential for achieving unbiased results with stochastic imputation. Furthermore, we address the concern that this practice constitutes “double dipping” or data dredging. By providing both theoretical and empirical evidence, we show why including the outcome variable is a legitimate and necessary approach rather than a source of bias.
Read moreUntangling Causal Effects: Understanding the Limits of Statistics

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. The second half of the talk will focus on how statistical techniques can be leveraged to examine the impact of a potential unmeasured confounder. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders, introducing the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.
Read moreIt's ME hi, I'm the collider it's ME

This talk will focus on framing measurement error as a collider from a causal inference perspective. We will begin by demonstrating how to visually display measurement error in directed acyclic graphs (DAGs). We will then show how these graphs can be used to help communicate when corrections for measurement error are needed and how to implement these corrections in order to estimate unbiased effects. Finally, we will demonstrate how sensitivity analyses traditionally used to address omitted variable bias can be used to quantify the potential impact of measurement error.
Read moreTeaching
STA 112 -- WFU Spring 2024

Introduction to Regression and Data Science. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Read moreBEM 392 -- WFU Spring 2024

Seminar in Mathematical Business Analysis. The main purpose of this seminar is to develop the capability to apply quantitative knowledge to real and ill-defined problems. It tries to bridge the gap between the theory of quantitative decision approaches such as management science/operations research, information systems, and statistics (now mainly collected in the Business Analytics field), with the application of these approaches to the solution of actual business problems.
Read moreSTA 779 -- WFU Fall 2023

Causal Inference. From Correlation to Causation. The goal of this course is to give students the skills needed to conduct analyses and communicate results when causality is the goal. Students will learn how to implement causal inference techniques including matching and weighting, evaluate assumptions, and conduct sensitivity analyses.
Read moreWriting
Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems
The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like Julia, Python, and R to perform tasks such as data cleaning, manipulation, and predictive modeling. The Jamboree showcased the educational benefits of working with open data, providing participants with practical, hands-on experience.
Read morePartnering with Authors to Enhance Reproducibility at JASA
The 'Why' behind including 'Y' in your imputation model
Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true.
Read more