Causal Graphs Explained: LLMs and Graph Science with Michelle Yi

Understanding Causal Graphs

• 💡 Causal graphs are a tool to understand relationships beyond simple correlation, moving towards understanding interventions and confounding variables.
• 🎯 The classic example of shark attacks and ice cream sales illustrates how correlation does not imply causation, highlighting the need for causal modeling.
• 🧠 The challenge lies in identifying true causes versus confounding variables, which are often influenced by a third factor, like summer driving both beach swimming and ice cream sales.

LLMs in Causal Graph Construction

• 🚀 Large Language Models (LLMs) can assist in constructing causal graphs by structuring data and reducing the manual labor involved.
• 🛠️ Tools like NetworkX or Kovz can be used for building graphs, but getting the structure right has historically been a significant barrier.
• ✅ LLMs help overcome this barrier by automating the process of organizing data into a format suitable for answering questions about confounding variables and effective interventions.