How Karl Popper can make you as good a data scientist as George Soros

The Tao of Data Science column explores how centuries of philosophers have been tackling the key problems of machine learning and data science. Karl Popper is best known for the view that science proceeds by "falsifiability" -- the idea that one cannot prove a hypothesis is true, or even have evidence of truth by induction (yikes!), but one can refute a hypothesis if it is false. Suppose Popper was a modern data scientist and needed to implement a machine learning solution to predict some phenomenon of interest. Given his philosophy of science, how would he have proceeded to implement his model? Popper would implement a causal model.