Publications

This paper presents an extension to the Conservative PC algorithm which is able to detect violations of adjacency faithfulness under causal sufficiency and triangle faithfulness. Violations can be characterized by pseudo-independent relations and equivalent edges, both generating a pattern of conditional independencies that cannot be modeled faithfully. Both cases lead to uncertainty about specific parts of the skeleton of the causal graph. ...

Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic ...

This paper presents an extension to the Conservative PC algorithm which is able to detect violations of
adjacency faithfulness under causal sufficiency and triangle faithfulness. Violations can be characterized by pseudo-independent relations and equivalent edges, both generating a pattern of conditional independencies that cannot be modeled faithfully. Both cases lead to uncertainty about specific parts of the skeleton of the causal graph. ...

Constraint-based causal structure learning algorithms rely on the faithfulness property.
For faithfulness, all conditional independencies should come from the system's causal structure.
The problem is that even for linear Gaussian models the property is not tenable.
In this paper, we identify 4 non-causal properties that generate conditional independencies and investigate whether they can be recognized by patterns in the conditional ...

In this paper we analyze the relationship between faithfulness and the more recent condition of algorithmic Independence of Conditionals (IC) with respect to the Conditional Independencies (CIs) they allow. Both conditions have been extensively used for causal inference by refuting factorizations for which the condition does not hold. Violation of faithfulness happens when there are
CIs that do not follow from the Markov condition. For ...

Algorithms for Markov boundary discovery from data constitute an important recent development
in machine learning, primarily because they offer a principled solution to the variable/feature selection
problem and give insight on local causal structure. Over the last decade many sound algorithms
have been proposed to identify a single Markov boundary of the response variable. Even though
faithful distributions and, more ...

This paper analyses Yan Fu’s translation of the title and the key terms in Thomas Huxley’s Evolution and Ethics and shows that his unfaithfulness was mainly due to his personal intention to inspire the Chinese people to fight against foreign enemies and the feudal system in late nineteenth-century China. In his famous The Heavenly Theory of Evolution, the translation of Evolution and Ethics, Yan Fu added the traditional Chinese value of ‘heaven’ ...