Causal Data Science

Adam Kelleher, Medium, Sept 07, 2016
Commentary by Stephen Downes
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Nice little four-part series on causation and correlation based on the work Causality by Judea Pearl. It's a probabilistic approach to causation. "We stop talking about things as being completely determined by the causes we take into account. Instead, we talk about a cause as increasing the chances of its effect." This is the only way to even begin to think of causation in complex environments, though it requires understanding the essentials of graphs and conditional probability. Consequently this series progresses from 'intuitively clear' to 'gaaaah'. The most important article of the set for educators is probably the second one, which deals with bias. "If you’re doing a survey study at a college, there can be bias due to the fact that everyone has been admitted."

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