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1/3 When you have a chance, please explain to readers on this Twitter how Bayesian Data Analysis (BDA) can help one think about causality. I have heard it from many statisticians and data analysts but I have never been able to understand what they find to be helpful and why.
2/3 Is it the "model selection" part offered by BDA? Or the idea that you are properly combining prior knowledge with data? In my opinion, BDA is a siren song that lure people away from properly "thinking" about causation, as I argue here ucla.in/2nZN7IH and in many
3/3 other forums. I am appealing to you because, as an accomplished reader of #Bookofwhy I think you would be able to pin point to us where precisely BDA enthusiasts see the connection to Causal Inference, and why I am missing this connection. As always, a toy example is the KEY.
