  The aim of this paper is to discuss a recent result which shows that
probabilistic inference in the presence of (unknown) causal mechanisms can be
tractable for models that have traditionally been viewed as intractable. This
result was reported recently to facilitate model-based supervised learning but
it can be interpreted in a causality context as follows. One can compile a
non-parametric causal graph into an arithmetic circuit that supports inference
in time linear in the circuit size. The circuit is also non-parametric so it
can be used to estimate parameters from data and to further reason (in linear
time) about the causal graph parametrized by these estimates. Moreover, the
circuit size can sometimes be bounded even when the treewidth of the causal
graph is not, leading to tractable inference on models that have been deemed
intractable previously. This has been enabled by a new technique that can
exploit causal mechanisms computationally but without needing to know their
identities (the classical setup in causal inference). Our goal is to provide a
causality-oriented exposure to these new results and to speculate on how they
may potentially contribute to more scalable and versatile causal inference.
