In this episode, we will explore the foundational concepts of causal analysis, focusing on its two main pillars: causal discovery and causal inference. We will discuss the types of questions these pillars aim to answer and provide illustrations of related methodologies to better clarify their concepts.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/causal-analysis-overview-causal-inference-versus-experimentation-versus-causal-discovery-d7c4ca99e3e4