Does anyone in 2020 believes that linear regr can be used for causal discovery?
Hot take: today, little consensus on how to do #causalinference
@yudapearl emphasizes “qualitative” graphical model based approach
@blei_lab emphasizes latent factor analysis for ‘surrogate causes’
@StatModeling: linear regr for “quantitative” causal discovery
other results !pic.twitter.com/oqzdFy30t4
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Thanks for putting this in so crisp words. That is what I actually wished to discuss.
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I am pretty sure that
@yudapearl and@StatModeling mean the same thing and just use different words and approaches (i.e. you can use@yudapearl approach to show that@statmodeling does it right). -
I am not so sure. Why? Because all my attempts to convince
@statmodeling to show us how he solves a simple causal problem (one whose solution is known in advance) ended up in failure. I lost my charm. Simpson's would be a good example#Bookofwhy - Još 5 drugih odgovora
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I think you need to add
@hectorzenil & algorithmic probability to the listHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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