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1/4 I believe this set of slides reinforces what I tweeted earlier: -- its hard to cut the embilical chord to Mother-Stat. It occurred to me that this urgency to stay in Stat-womb was also the motivation behind the potential-outcome framework. The benefits
2/4 were obvious, nothing is new, Y_1 and Y_0 are ordinary variables, with some missing values, so what? Everything else is ordinary statistics. The price, of course, was (1) Everything was tied to experimental "treatments", not to "events" or absence of events, and (2) we need
3/4 to express knowledge in the language of {Y_1, Y_0} , namely, in the formidable language of "conditional ignorability". Some would argue: What's wrong in letting statisticians broaden the scope of statistics and then believe that "it is all statistics"? I believe PO is a good
4/4 of what could go wrong. Distinctions play a role in science. Treating the Ladder of Causation as one chunk, in the name of "It is all statistics", while ignoring theoretical barriers between the 3 levels creates more confusion than the stat-womb warmth can sooth. #Bookofwhy
