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by roenxi
697 days ago
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> Hm, time series series regression is a standard, accepted approach to causal inference But statistical causality - things like Granger causality for example - aren't, in reality, establishing causality. They're statistical properties. You can't ever establish causality from statistical data. Eg, if I light a log on fire there will be bright light and later on there will be ash. If you have a timeseries of luminosity and quantity of ash present, bright light will be Granger-causal of ash. But in reality we know that bright light isn't causing the ash; the situation is we are analysing a bonfire. You've got a group of people there in that analysis article that aren't very good at interpreting results. They're looking at a time of extreme turmoil, they've picked 2 random timeseries that are responding to underlying causes and assuming that they are the entire story. They can't do that, it isn't a valid argument. It isn't a thorough enough treatment. In analogy, they're missing the fire for the light. There isn't particularly strong evidence that unions do anything on their own at the macro level; especially since the economic regime was just very different in an era where the available energy supplied was cheap and quantity was rapidly increasing. > For me, it suffices to say that the authors did not weakly position their argument as you claimed. I never said they weakly positioned their argument, their argument is watertight, they developed a data set and analysed it. Found a bunch of interesting statistical facts. Solid academic work. camdat weakly positioned his argument. |
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This isn't an experimental study, and so they have to rely upon plausibility in context. This explains their multi-faceted approach a la distributional decompositions and state and IV.
To me, the contrarian position — that unions have no such effect — doesn't look as good. Prove it :)