Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data
Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tools select an optimal method for a dataset, and recent ensembles aggregate multiple causal-discovery algorithms into one graph, but little work pools evidence across different mathematical traditions, including non-causal ones. We present Multi-Method Causal Evidence Synthesis (MCES), a framework that ranks which candidate drivers in an observational system are most likely relevant to a set of outcomes, and with what strength of evidence. MCES runs eleven met
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- PossiblePossibly related (embedding) · 50%Anti-Causal Domain Generalization: Leveraging Unlabeled Data - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 47%Netflix Open-Sources oci-agent For Observational Causal Inference - Open Source For You →
- FuzzyOverlapping authors or contributors · 62%microsoft/ML-For-Beginners →
“Shared author/contributor keys: gupta”
- LinkedLinked via arxiv author · 85%Manish Gupta →
“Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational”
- LinkedLinked via arxiv author · 85%Dipanjan De →
“Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational”