This talk will cover the application of the causal data science pipeline in practice, with a focus on retail use cases. Along the way, I will share learnings and tips from bringing the graphical causal approach into retail data science,  and I will highlight important nuances and distinctions in the underlying concepts and methods involved in this. This will include demonstrating the use of open-source causal libraries and tools, and learnings for how these can best fit into the lifecycle of a causal data science project.