In this episode, we explore how Klaviyo used counterfactual learning and uplift modeling to move beyond the question of which treatment works — to the deeper question of for whom it works. We’ll see how the team combined randomized experiments, causal inference techniques, and uplift modeling to power a product that helps marketers deliver smarter, more personalized messages.
For more details, you can refer to their published tech blog, linked here for your reference: https://klaviyo.tech/the-stats-that-tell-you-what-could-have-been-counterfactual-learning-and-uplift-modeling-e95d3b712d8a