What are the techniques to discriminate between coincidences, correlations, and real causation? Also, if you have a model where the response is highly correlated to (say) 5 non-causal variables and 2 direct causal variables, how do you assign a weight to the two causal variables?
Can you provide examples of successful cause detection? In which contexts is it important to detect the true causes? In which context causation can be ignored as long as your predictive model generates great ROI?