Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Learning Bayesian Networks
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Detection of Unfaithfulness and Robust Causal Inference
Minds and Machines
Hi-index | 0.00 |
The faithfulness condition (FC) is a useful principle for inferring causal structure from statistical data. The usual motivation for the FC appeals to theorems showing that exceptions to it have probability zero, provided that some apparently reasonable assumptions obtain. However, some have objected that, the theorems notwithstanding, exceptions to the FC are probable in commonly occurring circumstances. I argue that exceptions to the FC are probable in the circumstances specified by this objection only given the presence of a condition that I label homogeneity, and furthermore that this condition typically does not obtain in the FC's intended domain of application.