Book Review: Why – A Guide to Finding and Using Causes

A new book, “ Why: A Guide to Finding and Using Causes ,” by Stevens Institute of Technology assistant professor of computer science Samantha Kleinberg is a necessary addition to any data scientist’s bookshelf as it helps bring focus to the dreaded “correlation does not imply causation” conundrum that affects our understanding of data-centric problems. The best outcome of Big Data analytics, or of any computational model, is a number of correlations each with a level of confidence that the correlation holds true in the real world or at least the world represented by the data. But to determine if a correlation is true in the real world, it must be verified empirically. This can be viewed as the First Law of Data Science and a good reason why this…


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