  Dataflow computing was shown to bring significant benefits to multiple niches
of systems engineering and has the potential to become a general-purpose
paradigm of choice for data-driven application development. One of the
characteristic features of dataflow computing is the natural access to the
dataflow graph of the entire system. Recently it has been observed that these
dataflow graphs can be treated as complete graphical causal models, opening
opportunities to apply causal inference techniques to dataflow systems. In this
demonstration paper we aim to provide the first practical validation of this
idea with a particular focus on causal fault localisation. We provide multiple
demonstrations of how causal inference can be used to detect software bugs and
data shifts in multiple scenarios with three modern dataflow engines.
