FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions
We study fully online fixed-confidence causal discovery without any historical observational data. Starting from zero samples, the learner sequentially selects interventions to recover both the causal DAG and its edge weights under a linear-Gaussian structural equation model. We establish an instance-dependent lower bound for any $(ε,δ)$-correct algorithm and propose \textsc{FOCUS}, which adaptively allocates interventions through an online max--min game. A key contribution is a computable concentration inequality for the accumulated KL divergence involving causal parameters shared across interventions. We prove that \textsc{FOCUS} is $(ε,δ)$-correct and that its expected stopping time ma...
| Authors | Haijie Xu, Chen Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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