Why every data scientist shall read “The Book of Why” by Judea Pearl

I have been a big fan of machine learning for 4 years and deep learning for more than a year. I built predictive model for fun and work. I know a lot of algorithms, from the conventional one like gradient boosting to the deepest model like LSTM.

Despite numerous algorithms I had acquired, my puzzle remains. Puzzle That Algorithm Itself Cannot Solve. If you are not the kind of data scientist who only cares how to reduce that 0.01% error but try to make sense of your model, you might have questioned yourselves from time to time: 1. Should I add this variable to my model? 2. Why does this counter-intuitive variable show up as a predictive one?

Author: Ken Tsui

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