Samantha Kleinberg

The phrase "correlation is not causation" is one of the most famous phrases in the world of statistics, and science: anyone who did a bit amount of reading will complete the sentence before you can finish saying the second word.

But if correlation is not causation, then what on earth is causation? Or, is it even possible to have causation without correlation? The questions sound dead simple, yet they have such depth and importance that researchers are still trying to come up with explanations and techniques to clarify them. From trying to find whether a medication is really effective, to illuminating mysterious criminal cases, from trying to make predictions of future events based on past time series to making policy decisions for societies in order to get the intended effect, "finding and using causes" is an essential aspect, full of intellectual pitfalls.

One has to tread carefully in this area, and this book can be considered a good introduction to the topic. It is intended for people who are not experts on causality; avoiding rigorous technical explanations from the relevant fields, preferring to motivate the intuition of the reader by giving many real-world examples, some of them pretty surprising. It is unfortunate that it sometimes becomes repetitive and wordy, a good editor would probably be able to cut down 20% of the material without loss of the critical information and messages it conveys.

After reading the book, if you developed a more critical view towards popular science reporting such as "X therapy is found to cause Y", "politicians want to enact X so that society will be better off", etc., then I think the book will have achieved its goal. However, to go beyond that, you'll have to follow the contents of the extensive notes and references provided by the author, or read one of her more technical book "Causality, Probability, and Time".… (más)