Publication Library
An Introduction to Causal Inference
Description: Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. In this paper, I provide a concise introduction to the graphical approach to causal inference, which uses Directed Acyclic Graphs (DAGs) to visualize, and Structural Causal Models (SCMs) to relate probabilistic and causal relationships. Successively, we climb what Judea Pearl calls the “causal hierarchy” — moving from association to intervention to counterfactuals. I explain how DAGs can help us reason about associations between variables as well as interventions; how the do-calculus leads to a satisfactory definition of confounding, thereby clarifying, among other things, Simpson’s paradox; and how SCMs enable us to reason about what could have been. Lastly, I discuss a number of challenges in applying causal inference in practice.
Created At: 04 November 2024
Updated At: 04 November 2024
Marc Andreessen on AI Geopolitics and the Regulatory Landscape
Description: Marc Andresseen is the co-founder of Andressen Horowitz. In this interview, Marc dives deep into how AI will reinvent almost every product category we understand today and has the potential to reshape geopolitics, biology, and defense. Throughout the chat, Andreessen and Nishihara explore the technical challenges ahead, including the policy landscape, fights to outlaw open source AI, and lessons from Europe's history of technology innovation and regulation.
Created At: 04 November 2024
Updated At: 04 November 2024