Intervention Target Estimation (ITE) is vital for both understanding and decision-making in complex systems, yet it remains underexplored.

Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.

Often, observational data alone is not enough to uniquely identify a system's causal structure.

Other concurrent works address, e.g., learning from soft interventions with polynomial mixing [

Therefore, it's important to study whether we can and also how to

Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof.