Causal inference as a blind spot of data scientists
I’ve tried to understand causal inference several times and failed. Tutorials seem unnecessarily long winded. I wish authors would give simple, to the point examples.<p>Say I have a simple table of outdoor temperatures and ice cream sales.<p>What can the machinery of causal inference do for me in this situation?<p>If it doesn’t apply here, what do I need to add to my dataset to make it appropriate for causal inference? More columns of data? Explicit assumptions?<p>If I can use causal inference, what can it tell me? If I think of it as a function CA(data), can it tell me if the relationship is actually causal? Can it tell me the direction of the relationship? If there were more columns, could it return a graph of causal relationships and their strength? Or do I need to provide that graph to this function?<p>I know a wet pavement can be caused by rain or spilled water or that an alarm can go off due to an earthquake or a burglary. I have common sense. I also understand the basics of graph traversal from comp sci classes.<p>How do I practically use causal inference?<p>To the authors of future articles on this (or any technical tutorial), please explain the essence, the easy path, then the caveats and corner cases. Only then will abstract philosophizing make sense.