AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
The Latent Causal Model Theorem
Theory says if you have two models and then a map that takes these causal variables and maps them to some data space so think pixels. So we call this a latent causal model. Now what the theorem says is both of these latent causal models give rise to the same kind of data setif you look at them. There's the same distribution. The hidden assumption here is that indeed nature operates as such a causal model, but sometimes our assumptions are not satisfied.