I couldn't disagree more, especially about Russia example because the Russian war qualifies, by application of objective criteria from UN conventions and ICC, a

I couldn't disagree more, especially about Russia example because the Russian war qualifies, by application of objective criteria from UN conventions and ICC, as a genocide. You're right about the murders example, but this is not related at all to the above debate and, again, a deviation. Whether Elon Musk is an outlier and exceptionally evil as a tech billionaire or whether every tech billionaire is as evil as him has absolutely not the slightest bearing on his evilness. That's why the accusation of whataboutism is perfectly adequate and reasonable in this case. As I said in my initial post, the moral independence of actions is codified in law and common practice. The "others did it too" defense does not work in criminal court, and I can assure you that it will also not work for Russia. > Correct, and this is a valid argumentative technique. It's provably a fallacy. On a side note, framing has nothing to do with making fallacious arguments, it only pertains to how you express arguments. "The glass is half full" vs "The glass is half empty" -- that's framing. Two different arguments about the same topic -- that's not framing. It also has nothing to do with the baserate fallacy. If someone commits a baserate fallacy, you simply point out that they've committed a baserate fallacy. This only makes sense for statistical arguments. Framing and categorization are also two totally different terms for different application domains. Framing does not concern the categories and population you choose when making statistical inferences. I don't know why people misuse the term all the time, it's not hard to understand if you look at the original literature in experimental psychology. Framing is also not related in any obvious way with causal structure/causal models. Moreover, your example about Elon Musk is not about causal structure, it concerns a statistical property; at least, I cannot see how a causal model can be used to support "usual" vs. "unusual" attributions. However, I grant you that maybe it could be spelled out in causal modeling terms, for example in Judea Pearl's approach, but it's not obvious how to do that. Anyway, for me this discussion is closed. You're not very persuasive because you're using the wrong terminology. As I said, I'm interested in this topic also from a professional perspective. I know what I'm talking about (whether you believe me or not). I'll keep it at that and close this conversation.