Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Identifying conditional causal effects
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On the testable implications of causal models with hidden variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Testing identifiability of causal effects
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Probabilistic evaluation of sequential plans from causal models with hidden variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of non-observable (i.e., hidden or latent) variables. It is well known that the answer to the question depends on the structure of the causal Bayesian network, the set of observable variables, the set of effect variables, and the set of intervention variables. Sound algorithms for identifiability have been proposed, but no complete algorithm is known. We show that the identify algorithm that Tian and Pearl defined for semi-Markovian models (Tian and Pearl 2002, 2002, 2003), an important special case of causal Bayesian networks, is both sound and complete. We believe that this result will prove useful to solve the identifiability question for general causal Bayesian networks.