Causal inference techniques are crucial to understanding the impact of actions on outcomes. This talk shares lessons learned from applying these techniques in real-world scenarios where standard methods do not immediately apply. Our key question is: What is the causal impact of wealth planning services on a network of individual’s investments and securities? We'll examine the challenges posed by practical constraints and show how to deal with them before applying standard approaches like staggered difference-in-difference.

This self-contained talk is prepared for general data scientists who want to add causal inference techniques to their toolbox and learn from real-world data challenges.