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Looking at observational data, you just can’t infer the causality from that, the best you can do is to try to make your data, you try to make your model as interpret the world as possible and then it’s essentially a rhetorical act to try to figure out from the observational model, what might be causal about it? And the case at the time is something like you can predict which people are gonna cancel, but that’s predicting what’s gonna happen in the absence of some intervention and treatment, which what you really want to know is that what is the intervention or treatment that would minimise that probability, that’s also true in drug data, so you can look at, you know, just observational studies about different people using drugs and then the outcomes. And it’s been shown in the David Madigan at Columbia has done some great work showing that depending on what you control for in your model, you can make the sign of a drug be positive or negative that is just basically looking at people in the wild as opposed to people in a randomized control trail, there is just no way to figure out if a particular medical treatment or medical drug is actually increasing or decreasing the risk of some morbidity, some bad effect, so regardless of how big the data are. It’s really a tough problem to try to impose some sort of causal meaning. This was no longer before big data, this is in statistics, we talk, or they talk about structure modelling as opposed to predictive modelling, and it’s getting from prediction to prescription is a, is a real philosophical break.
