Causal Inference Insights
Emphasizing the importance of causal methods, Sean argues that all data scientists can benefit from framing their problems as causal questions. He suggests that even if the answers remain unchanged, formalizing this approach enhances understanding. Additionally, he highlights the value of diverse tools and methodologies in causal modeling, advocating for a holistic mindset and the use of causal graphs and experimentation.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 617: Causal Modeling and Sequence Data — with Sean Taylor
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