Paragraph about Scientific software

These contentions about the connection between causal claims and
statistical relevance relations are consequences of a more general
principle called the Causal Markov condition which has been
extensively discussed in the recent literature on
causation.^{[ 10 ]}
A set of variables standing in a causal relationship and an
associated probability distribution over those variables satisfy the
Causal Markov condition if and only if conditional on its direct
causes every variable is independent of every other variable except
possibly Scientific software for its effects. Two relevant points have emerged from
discussion of this condition. The first, which was in effect noted by
Salmon himself in work subsequent to his (1971), is that there are
circumstances in which the Causal Markov condition fails and hence in
which causal claims do not imply the screening off relationships
described above. This can happen, for example, if the variables to
which the condition is applied are characterized in an insufficiently
fine-grained
way.^{[ 11 ]}
The second and more fundamental observation is that, depending on the
details of the case, many different sets of causal relationships may
be compatible with the same statistical relevance relationships. For
example, a structure in which *B* causes mathlab *A* which in
turn causes *S* will, if we assume the Causal Markov condition
(that is, make assumptions like Salmon's connecting causation and
statistical relevance relationships), lead to exactly the same
statistical relevance relationships as in the example in which
*A* is a common cause of *B* and *S*. Similarly
if *S* causes *A* which in turn causes *B*. In
structures with more variables, this underdetermination of causal
relationships by statistical relevance relationships may be far more
extreme. Thus a list of statistical relevance relationships, which is
what the *SR* model provides, need not tell us which causal
relationships are operative. To the extent that explanation has to do
with the identification of the causal relationships on which an
explanandum-outcome depends, the *SR* model fails to fully
capture these.