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Julia M. Rohrer@dingdingpeng.the100.ci |
If an effect falls in a forest and no one is there to determine the mechanism, is it even causal? New post in which I try to clarify some things--claims about causal effects are indifferent to mechanisms; heterogeneity does not invalidate average estimates. www.the100.ci/2024/06/26/s...
8 replies 24 reposts 50 likes
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jon ben-menachem
@jbenmenachem.com
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I think mechanisms are the best way we can gauge whether a model producing a causal effect estimate are in accordance with theory (i.e., credible)
0 replies 0 reposts 1 likes
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Klaus Pforr
@klauspforr.bsky.social
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You could refer your children to this blog post with their never-ending why-questions
0 replies 0 reposts 2 likes
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Sean
@publichealth.bsky.social
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oof ouch owie my head
0 replies 0 reposts 2 likes
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Tim Morris
@timpmorris.bsky.social
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Another banger Julia! Interesting point on 'starts from the opposite notion that causal effects are the same for everyone'. Seems similar in experimental design literature. Often 'assuming this model [where all individual causal effects are identical], then the optimal design…'
1 replies 0 reposts 2 likes
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Steve Haroz
@steveharoz.com
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I'm gonna reuse this 🤣
2 replies 1 reposts 8 likes
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Darren Dahly
@statsepi.bsky.social
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Another 2 cents on looking to phantom HTE to save a "null" finding:
statsepi.substack.com/p/perils-of-...
0 replies 1 reposts 4 likes
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Jay Patel
@infotainment.bsky.social
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Reminds me of Wittgenstein for some reason: "a wheel that can be turned though nothing else moves with it is not part of the mechanism”(§271)."
0 replies 0 reposts 1 likes
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@tailcalled.bsky.social
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This is a good introduction to the potential outcomes framework, though I worry that the potential outcomes framework fails to zoom in on some of the regularities that make causal inference more tractable.
1 replies 0 reposts 1 likes