Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023)
Causal Bandits Podcast
Unraveling Causality: Challenges in Discovery and Modeling
This chapter explores the intricate relationship between causality and various scales, emphasizing the differences in causal inference at different temporal and spatial resolutions. It discusses the limitations of existing methodologies, the significance of gold standard datasets, and the challenges posed by synthetic versus real-world data in causal discovery. Furthermore, the chapter highlights ongoing developments and methodologies for optimizing causal graphs, particularly in complex systems like supply chain management.
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