Relaxing Faithfulness with Intervention-Only Causal Discovery
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of p
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- PossiblePossibly related (embedding) · 56%erdogant/bnlearn →
- PossiblePossibly related (embedding) · 51%py-why/EconML →
- PossiblePossibly related (embedding) · 49%Anti-Causal Domain Generalization: Leveraging Unlabeled Data - Apple Machine Learning Research →
- LinkedLinked via arxiv author · 85%Bijan Mazaheri →
“Relaxing Faithfulness with Intervention-Only Causal Discovery”
- LinkedLinked via arxiv author · 85%Jiaqi Zhang →
“Relaxing Faithfulness with Intervention-Only Causal Discovery”
- LinkedLinked via arxiv author · 85%Caroline Uhler →
“Relaxing Faithfulness with Intervention-Only Causal Discovery”
- PossiblePossibly related (embedding) · 50%cdt15/lingam →