Test-Time Learning of Causal Structure from Interventional Data
This addresses a key challenge in causal inference for researchers and practitioners, though it appears incremental as it builds on existing supervised causal learning and test-time training methods.
The paper tackles the problem of causal discovery from interventional data when intervention targets are unknown, proposing TICL which combines test-time training with joint causal inference to improve generalization, achieving superior performance on bnlearn benchmarks.
Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention targets are unknown. To address this, we propose TICL (Test-time Interventional Causal Learning), a novel method that synergizes Test-Time Training with Joint Causal Inference. Specifically, we design a self-augmentation strategy to generate instance-specific training data at test time, effectively avoiding distribution shifts. Furthermore, by integrating joint causal inference, we developed a PC-inspired two-phase supervised learning scheme, which effectively leverages self-augmented training data while ensuring theoretical identifiability. Extensive experiments on bnlearn benchmarks demonstrate TICL's superiority in multiple aspects of causal discovery and intervention target detection.