k-Order Relaxation of Faithfulness Assumption for Markov Blanket Discovery
A recent study available on arXiv suggests a k-order relaxation of the faithfulness assumption for deriving graphical Markov blankets from datasets. This assumption, which indicates that conditional independencies in a distribution align with separations in the graphical model, is frequently breached by higher-order dependencies, including XOR and parity relations, as well as by empirical violations from finite samples that can create misleading dependencies. The researchers propose a relaxation that accounts for parity-type interactions among k+2 variables and introduce a proof-of-concept algorithm named k-order Markov blanket (kOMB). The paper can be found under the identifier arXiv:2607.26357v1.
Key facts
- Paper proposes a k-order relaxation of the faithfulness assumption.
- Faithfulness assumption is commonly violated by higher-order dependencies like XOR and parity-type relations.
- Finite-sample empirical violations can induce spurious dependencies.
- Relaxation captures parity-type relationships between k+2 variables.
- Proof-of-concept algorithm called k-order Markov blanket (kOMB) is presented.
- Paper available on arXiv with ID 2607.26357v1.
- Application areas include structure learning, causal discovery, and feature selection.
Entities
Institutions
- arXiv