To do or not to do: finding causal relations in smart homes
  Research in Cognitive Science suggests that humans understand and represent
knowledge of the world through causal relationships. In addition to
observations, they can rely on experimenting and counterfactual reasoning --
i.e. referring to an alternative course of events -- to identify causal
relations and explain atypical situations. Different instances of control
systems, such as smart homes, would benefit from having a similar causal model,
as it would help the user understand the logic of the system and better react
when needed. However, while data-driven methods achieve high levels of
correlation detection, they mainly fall short of finding causal relations,
notably being limited to observations only. Notably, they struggle to identify
the cause from the effect when detecting a correlation between two variables.
This paper introduces a new way to learn causal models from a mixture of
experiments on the environment and observational data. The core of our method
is the use of selected interventions, especially our learning takes into
account the variables where it is impossible to intervene, unlike other
approaches. The causal model we obtain is then used to generate Causal Bayesian
Networks, which can be later used to perform diagnostic and predictive
inference. We use our method on a smart home simulation, a use case where
knowing causal relations pave the way towards explainable systems. Our
algorithm succeeds in generating a Causal Bayesian Network close to the
simulation's ground truth causal interactions, showing encouraging prospects
for application in real-life systems.
