Faithfulness refers to the property of a statistical model that ensures the absence of unmeasured confounding between variables in a directed acyclic graph (DAG). This concept is crucial because it allows researchers to make valid inferences about causal relationships, as it implies that if a causal relationship is present, there will be corresponding dependencies among the variables. In simpler terms, faithfulness helps to guarantee that the relationships depicted in the graph are not misleading and reflect true associations in the data.
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