Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Observational causal discovery is only identifiable up to the Markov equivalence class. While interventions can reduce this ambiguity, in practice interventions are often soft with multiple unknown targets. In many realistic scenarios, only a single intervention regime is observed. We propose a scalable causal discovery model for paired observational and interventional settings with shared underlying causal structure and unknown soft interventions. The model aggregates subset-level PDAGs and applies contrastive cross-regime orientation rules to construct a globally consistent maximal PDAG under Meek closure, enabling generalization to both in-distribution and out-of-distribution settings....
| Authors | Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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