On the Testable Implications of Causal Models with Hidden Variables

Understand

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.