Causal Representation Learning from Multiple Distributions: A General Setting

In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the hidden causal variables (e.g., the underlying concepts or objects). For the purpose of making predictions in changing environments or making proper changes to the system, it is helpful to recover the hidden causal variables $Z_i$ and their causal relations represented by graph $\mathcal{G}_Z$. This problem has recently been known as causal representation learning. This paper is concerned with a general, completely nonparametric setting of causal …

concepts cs.lg environments functions general hidden image making multiple objects pixels predictions representation representation learning stat.ml variables