  We present a basis for studying questions of cause and effect in statistics
which subsumes and reconciles the models proposed by Pearl, Robins, Rubin and
others, and which, as far as mathematical notions and notation are concerned,
is entirely conventional. In particular, we show that, contrary to what several
authors had thought, standard probability can be used to treat problems that
involve notions of causality, and in a way not essentially different from the
way it has been used in the area generally known (since the 1960s, at least) as
'applied probability'. Conventional, elementary proofs are given of some of the
most important results obtained by the various schools of 'statistical
causality', and a variety of examples considered by those schools are worked
out in detail. Pearl's 'calculus of intervention' is examined anew, and its
first two rules are formulated and proved by means of elementary probability
for the first time since they were stated 25 years or so ago.
  Note: Corrected and extended parts of this paper will soon be published as a
book of the same title.
