  In this short note, I derive the Bell-CHSH inequalities as an elementary
result in the present-day theory of statistical causality based on graphical
models or Bayes' nets, defined in terms of DAGs (Directed Acyclic Graphs)
representing direct statistical causal influences between a number of observed
and unobserved random variables. I show how spatio-temporal constraints in
loophole-free Bell experiments, and natural classical statistical causality
considerations, lead to Bell's notion of local hidden variables, and thence to
the CHSH inequalities. The word "local" applies to the way that the chosen
settings influence the observed outcomes. The case of contextual
setting-dependent hidden variables (thought of as being located in the
measurement devices and dependent on the measurement settings) is automatically
covered, despite recent claims that Bell's conclusions can be circumvented in
this way.
