It'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.

Directed acyclic graphs (DAGs)

Measurement error

Quantifying bias

Sensitivity analysis