Virtual useR! 2024

Missing data are ubiquitous, pervasive, and often ignored in statistical analyses. Unfortunately, default methods such as complete case analysis may lead to biased and invalid results. This hands-on tutorial aims to equip data analysts with knowledge and skills to validly handle missing data using the popular R package {mice}. {mice} implements multiple imputation by chained equations, a flexible method for imputing (i.e. filling in) missing entries. The session will combine theoretical insights with hands-on exercises. Attendees will first learn the fundamentals of missing data theory, and then gain practical experience in addressing real-world missing data problems through guided demonstrations and exercises. Attendees are encouraged to bring their own incomplete datasets, to implement and evaluate their newfound skills. By the end of the session, attendees will be able to make informed decisions on how to validly handle missing data in their own data analysis projects.

In this tutorial, I’ll present an overview of our book, Causal Inference in R, freely available at r-causal.org. We’ll discuss the whole game, so to speak, of causal inference, following a few key steps: 1. Specify a causal question 2. Draw our assumptions using a causal diagram 3. Model our assumptions 4. Diagnose our models 5. Estimate the causal effect, and 6. Conduct sensitivity analysis on the effect estimate. We’ll discuss some new tools in the causal inference ecosystem, such as tipr, ggdag, propensity, halfmoon, and more, each making the act of causal inference easier and more principled.