It's not that hard. if the causality cannot make sense logically or plausibly, then you can reasonably reject it . no reasonable person would ever get the umbrella puddles thing confused.
> It's not that hard. if the causality cannot make sense logically or plausibly, then you can reasonably reject it . no reasonable person would ever get the umbrella puddles thing confused.
It's an illustrative example, taking it literally is missing the point. The reason you know it doesn't make sense for umbrellas to cause rain is that you already have an applicable causal model. The situations where you need to do causal inference are exactly those where you don't, and can't just rely on "reasonableness" or "plausibility".
This is not true. These causal methods generally require you to have a pre established framework for how the thing works. If you cannot supply additional variables that you, with your domain knowledge, know cover the confounding elements, it won’t do anything.
it’s Mathematical soup for trying to normalize out the effects of other variables to see what remains and calling it “causal”.
You should open a epidemiological journal these days. Half the papers are either as bad as "umbrellas causes puddles" or obviously confounded with socio-economic status.
It's an illustrative example, taking it literally is missing the point. The reason you know it doesn't make sense for umbrellas to cause rain is that you already have an applicable causal model. The situations where you need to do causal inference are exactly those where you don't, and can't just rely on "reasonableness" or "plausibility".