CausalML Book Ch6: Causal Inference via Linear Structural Equations

This episode introduces linear structural equation models (SEMs) and causal diagrams, also known as Directed Acyclic Graphs (DAGs). The text explains how these models can be used for causal inference, particularly in economics, using examples like gasoline demand and wage gap analysis. It highlights the importance of conditional exogeneity and the potential pitfalls of "collider bias" when conditioning on certain variables. The authors demonstrate how SEMs can distinguish between causal effects and mere statistical correlations, offering a framework to understand complex phenomena like discrimination.DisclosureThe CausalML Book: Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M., & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org; arXiv:2403.02467. Audio summary is generated by Google NotebookLM https://notebooklm.google/The episode art is generated by OpenAI ChatGPT