PAC-Causal Learnability: From Identification to Finite-Sample Guarantees
The PACC Discovery framework adapts PAC learning to causal inference, providing theoretical guarantees for discovering causal relationships from finite observational data. It formalizes how many samples are needed to achieve a desired accuracy in identifying true causal concepts, linking sample complexity to identification strength and model complexity.