A General Identification Condition for Causal Effects¶
Tian, J., & Pearl, J. (2002). A General Identification Condition for Causal Effects. Proceedings of the 18th National Conference on Artificial Intelligence (AAAI), 567-573.
Cited by¶
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Primes¶
- Identifiability
- In causal inference it is the do-calculus identification problem — given a causal DAG and a set of observed variables, when can an interventional distribution be expressed in terms of observed distributions? — and here it is decidable, with an algorithm returning either a formula or a proof of non-identifiability.
This sourceProvides a graphical criterion (later completed into a sound and complete algorithm) deciding when an interventional distribution is identifiable from observed distributions given a causal DAG.
- In causal inference it is the do-calculus identification problem — given a causal DAG and a set of observed variables, when can an interventional distribution be expressed in terms of observed distributions? — and here it is decidable, with an algorithm returning either a formula or a proof of non-identifiability.
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