On the Testable Implications of Causal Models with Hidden Variables
  The validity OF a causal model can be tested ONLY IF the model imposes
constraints ON the probability distribution that governs the generated data. IN
the presence OF unmeasured variables, causal models may impose two types OF
constraints : conditional independencies, AS READ through the d - separation
criterion, AND functional constraints, FOR which no general criterion IS
available.This paper offers a systematic way OF identifying functional
constraints AND, thus, facilitates the task OF testing causal models AS well AS
inferring such models FROM data.
