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PAC-Causal Learnability: From Identification to Finite-Sample Guarantees
Causal identification asks whether a causal target can be recovered from the observational distribution in an idealized infinite-data setting. In practice, only finite samples are available, and identifiability alone does not determine how much data are needed for reliable estimation. We introduce P
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