Causal Machine Learning
Exploring the nuances of causal machine learning reveals that understanding cause and effect requires more than just data; it necessitates domain knowledge and assumptions. While randomized control trials provide the gold standard for establishing causality, there are methods to infer causal relationships by conditioning on confounding variables. Sensitivity analyses can further illuminate potential confounders, emphasizing the importance of rigorous assumptions in drawing conclusions.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 613: Causal Machine Learning — with Emre Kiciman
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