Detection of Unfaithfulness and Robust Causal Inference

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Identifiability of Gaussian structural equation models with equal error variances (2012, background): “Furthermore, faithfulness in its full generality cannot be tested from data [Zhang and Spirtes, 2008].”
Learning Bayesian networks: approaches and issues (2011, background): “…learning methods to learn causal Bayesian networks from data means that assumptions are being made (implicit or explicit), such as the causal Markov condition or faithfulness condition (Spirtes et al., 2000), though new research has indicated possible weaker assumptions (Zhang & Spirtes, 2008).”
Identiﬁability of Causal Graphs using Functional Models (background): “Zhang and Spirtes [2007] analyse the testability of faithfulness.”
UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “Now, if a · b + c = 0 , the distribution is not faithful with respect to G 1 since we obtain X ⊥⊥ Z ; more precisely, it is not triangle-faithful (Zhang and Spirtes, 2008).”
Geometry of the faithfulness assumption in causal inference (2012, background): “The first condition (i) is called adjacency-faithfulness in [17], the second condition (ii) is called orientation-faithfulness.”
A survey of Bayesian Network structure learning (2021, background): “Zhang and Spirtes (2008) showed that a restricted assumption of faithfulness could be applied to the adjacency phase too.”