Causal Machine Learning
Discover how causal machine learning relies on domain knowledge to establish cause-and-effect relationships between variables. Emmer discusses the development of the Do y Python library and outlines the four essential steps of causal inference: modeling, identification, estimation, and refutation. Learn about the growing applications of causal ML in structured and unstructured data, and its significant contributions to agriculture, healthcare, and social sciences.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 613: Causal Machine Learning — with Emre Kiciman
Related Questions
Tell me about the podcast Super Data Science: ML & AI Podcast with Jon Krohn
Tell me about the podcast Super Data Science: ML & AI Podcast with Jon Krohn
Can you explain causality in machine learning as discussed in the episode SDS 469: Learning Deep Learning Together — with Konrad Körding and the clip Causality and Learning?