607: Inferring Causality
Super Data Science: ML & AI Podcast with Jon Krohn
Understanding Causality and Counterfactuals in Data Science
The chapter emphasizes the need for causal tools in data science to infer causality effectively, discussing the concept of counterfactuals to understand causal relationships by creating parallel worlds for comparison. It delves into the importance of randomization and balance in research studies for fair comparisons, highlighting the significance of conducting randomized control trials for confident causal inference. The speakers also discuss tools like BART for inferring causality and underscore the importance of engaging deeply with subject matter for effective machine learning in causal inference.
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