Casual Discovery for Product Analytics

I will discuss leveraging causal discovery in product analytics to uncover new insights and improve product development. First, I introduce a framework for categorizing causal questions, highlighting common challenges in product analytics, and emphasizing the need for discovering new causes that drive outcomes. I will present a process for generating hypotheses about causal relationships that are likely to lead to successful ideas for product improvements that can be validated through experiments. The key methodological insight is to model event data before they are aggregated, which helps us isolate causal relationships between events and allows for bias reduction to be automated under certain assumptions.

Please join the event. Everyone is welcome—it is free and you do not need to be affiliated with the university.

About Sean Taylor

Sean Taylor is a data scientist, social scientist, statistician, and software developer. He mostly specializes in methods for solving causal inference and business decision problems, and is particularly interested in building tools for practitioners working on real-world problems. He is a co-founder and chief scientist at Motif.