By the way , I think the data are very , very relieved to hear . So , but , but I'm going someplace very important here because if there's one topic that doesn't get enough attention in medicine , it's causality and uh causality is an obsession of mine . Like most of the day , on some level , I sit around thinking about causality .
So , but , but I'm going someplace very important here because if there's one topic that doesn't get enough attention in medicine , it's causality and uh causality is an obsession of mine . Like most of the day , on some level , I sit around thinking about causality . And I think the hardest part about studying medicine with respect to human beings is how difficult it is to infer causality for most things that we do So if you believe that smoking is causally related to lung cancer , then smoking cessation reduces the probability of lung cancer .
Um How do I make the best judgment about an individual from heterogeneous population data ? And based on what are causal and non causal inferences around risk ?
No , no , it's a huge red flag again . Um , just because something is causal doesn't mean it's , you're , you're guaranteed to get it . There are smokers who don't get lung cancer .
Like most of the day , on some level , I sit around thinking about causality . And I think the hardest part about studying medicine with respect to human beings is how difficult it is to infer causality for most things that we do So if you believe that smoking is causally related to lung cancer , then smoking cessation reduces the probability of lung cancer . That is a , that is a logical equivalency .
