Part 8/10:
To bridge this gap—achieving actionable insights from observational data—researchers often resort to causal inference. This process, while valuable, is labor-intensive and necessitates making assumptions that are hard to validate. DJ emphasizes that even thorough studies involve significant nuances, indicating the challenge of translating solid causal analyses into automated AI agent functions.
DJ also recounts personal experience from his consultancy, Truth Deta, where he makes use of causal models to predict user engagement behaviors effectively. These efforts demonstrate the intricate nature of causality and the importance of precision in modeling to achieve meaningful outcomes.