CollaborativeCausalDiscovery withAtomicInterventions
–Neural Information Processing Systems
Asinterventions areexpensive(require carefully controlled experiments) andperforming multiple interventions is time-consuming, an important goal in causal discovery is to design algorithms that utilize simple (preferably, single variable) and fewer interventions [Shanmugam et al.,2015]. However, when there are latents or unobserved variables in the system, in the worst-case, it is not possible to learn the exact causal DAG without intervening on every variable at least once.
Neural Information Processing Systems
Feb-9-2026, 04:54:21 GMT
- Country:
- Asia (0.04)
- Europe > United Kingdom (0.04)
- Genre:
- Research Report
- Experimental Study (0.54)
- Strength High (0.54)
- Research Report
- Industry:
- Health & Medicine (0.93)
- Technology: