> i always felt these probabilistic models were a conflation of correlation and causation > What do you mean? the example from the post of weather events is a

> i always felt these probabilistic models were a conflation of correlation and causation > What do you mean? the example from the post of weather events is a simplified version of what i mean the collected data expresses that there is a probability of 50/50 rain versus shine the first string of possible outputs is scattered and more random than the real data, so weights are introduced to nuance to the output and the second string is more closely representative of the data but this introduces a relative psuedo causation, what the author refers to as stickyness it's reminiscent of the monty hall problem(o) altering the state of the model causes its probability to change relative to a specific potential, but only within the limited scope of the probable space choosing one door causes the probabilities associated with the state to change but the system itself is unchanged by the choice and what you see with the weather is a simulation of the collected data stead the underlying system, which i feel conflates correlation with causation (o) https://en.wikipedia.org/wiki/Monty_Hall_problem https://en.wikipedia.org/wiki/Monty_Hall_problem