[Trombone12]

> My lesson from this: outcomes causal inference is very dependent on assumptions and methodologies, of which the options are many.

This seems like a natural feature of any sensitive method, not sure why this is something to complain about. If you want your model to always give the answer you expected you don't actually have to bother collecting data in the first place, just write the analysis the way pundits do.

[mmmmpancakes]

> This seems like a natural feature of any sensitive method, not sure why this is something to complain about.

I am exactly complaining it is sensitive. If theres robust alternatives why would i put this in prod?

[Trombone12]

Because you care about accuracy?

[mmmmpancakes]

Because with real world data like in production in tech there are so many factors to account for. Brittle methods are more susceptible to unexpected changes in the data or unexpected ways in which complex assumptions abut the data fail.

[mbowcut2]

But really, how accurate are your results if they depend on strong assumptions about your data?