Yeah. Thanks for the front door link, I’ll take some time learning this!
Maybe to reformulate a bit, in the second sub-scenario my idea was that each person has a kind of “tar thermostat”, which sets the desired level of tar and continually adjusts your desire to smoke. If some other factor makes you smoke more or less, it will compensate until your level of tar again matches the “thermostat setting”. And the trait that determines someone’s “thermostat setting” would also determine their cancer risk. Basically the system would counteract any external noise, making the statistician’s job harder (though not impossible, you’re right).
The third scenario, about skydiving, hints at a similar idea. The “thermostat” there is the person’s desire for thrill, so if you take away skydiving, it will try to find something else.
Oh I see, yeah this sounds hard. The causal graph wouldn’t be a DAG because it’s cyclic, in which case there may be something you can do but the “standard” (read: what you’d find in Pearl’s Causality) won’t help you unless I’m forgetting something.
An apparently real hypothesis that fits this pattern is that people take more risks / do more unhealthy things the more they know healthcare can heal them / keep them alive.
Yeah. Thanks for the front door link, I’ll take some time learning this!
Maybe to reformulate a bit, in the second sub-scenario my idea was that each person has a kind of “tar thermostat”, which sets the desired level of tar and continually adjusts your desire to smoke. If some other factor makes you smoke more or less, it will compensate until your level of tar again matches the “thermostat setting”. And the trait that determines someone’s “thermostat setting” would also determine their cancer risk. Basically the system would counteract any external noise, making the statistician’s job harder (though not impossible, you’re right).
The third scenario, about skydiving, hints at a similar idea. The “thermostat” there is the person’s desire for thrill, so if you take away skydiving, it will try to find something else.
Oh I see, yeah this sounds hard. The causal graph wouldn’t be a DAG because it’s cyclic, in which case there may be something you can do but the “standard” (read: what you’d find in Pearl’s Causality) won’t help you unless I’m forgetting something.
An apparently real hypothesis that fits this pattern is that people take more risks / do more unhealthy things the more they know healthcare can heal them / keep them alive.
The thermostat pattern is everywhere, from biology to econ to climate etc. I learned about it years ago from this article and it affected me a lot.