Using strong predictor in Model training?
I can think of two way inducing priors into supervised machine learning training process.
1) Using a Bayesian framework(PYMC for example) where you can define you random variables with whatever prior you like.
2) Engineering features that describe the needed relation. For example, in your instances I would create a new Boolean feature named 'is_strong_predictor_by_prior', and would assign true if x=s1 and y=d1 and false else wise. This new feature can hold multiple priors(not only the s1>>d1 relation).