In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.

However, it is known that even with infinite i.i.d.

Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there islittle empirical evidence that supports this claim.

We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.

We refer to them astarget-causing confounders.

Obviously in ANM-MM, all observations are generated by a set of g.m.s, which share the same functionform(f)butdifferinparametervalues(θ).

We argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also help in getting bettercausal models.