You’ve probably heard the old adage “correlation does not imply causation” but at some point we’ve got to say that drinking boiling hot tea and burning our tongues are more than just “strongly correlated”.

Enter Causal Inference, a collection of techniques for reasoning about the relationship between effects that can be measured and causes that can be identified. It helps us bridge between what we hear when we talk directly to users and what we observe about their behavior at scale and over time.

This talk will introduce some of the key techniques in causal inference and how they can be used by UX Researchers and Designers to understand potential users, current users, and the products we might build.