Causality and data science

An example of simulated data modeled for the CMS particle detector on the Large Hadron Collider (LHC) at CERN. (source: Via Lucas Taylor and CERN on Wikimedia Commons).Get 50% off the “Why” ebook with the code Data50. Causality is what lets us make predictions about the future, explain the past, and intervene to change outcomes. Despite its importance, it’s often misunderstood and misused. My new book Why aims to explain the reasons behind this with a jargon-free tour of causality: what is it, why is it so hard to find, and how can we do better at interpreting it? Understanding when our inferences are likely to be wrong is particularly important for data science, where we’re often confronted with observational data that is large and messy (rather than well-curated for…

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