Thinking out loud, it seems to me that for most things Bayesian logic supercedes Pearls causality. The probability of something given a prior, versus the probability without the prior is, in some sense, the degree to which the prior "causes" the result. The beauty is that you can usefully reason about P(A|B) without knowing P(A) or P(B).
On Thu., 23 Aug. 2018, 9:43 am Charles Haynes, <[email protected]> wrote: > You mention Bayesian statistics as a thing like Pearls causality maths > that's too complex.for most people and so hasn't caught on. I'd argue the > exact opposite. Bayesian statistics ARE complicated but the first time I > saw them my reaction was Oh My God this is going to change everything about > how I reason about anything. > > And it has. > > So it seems to me.that Pearls formalisms just aren't that useful. > > -- Charles > > On Thu., 23 Aug. 2018, 3:24 am Bharat Shetty, <[email protected]> > wrote: > >> On Wed, Aug 22, 2018 at 11:38 PM Landon Hurley <[email protected]> >> wrote: >> >> > Sorry to delurk with a massive rant but I love this field and Pearl's >> > work, and spent the last 18 months being denied my doctorate because I >> use >> > to much maths for a Psych department. >> > >> > >Anyone else have opinions on why his ideas haven't caught on more >> > >generally? >> > >> > >> > There are two connected problems (sorry, this area of statistics is my >> > field and raison d'etre, so bear with me) as to why Pearl's work isn't >> > universal. >> > >> > >> Thank you for sharing your insights and discussion! >> >> Regards, >> Bharat >> >
