Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilities of causation: Bounds and identification
Annals of Mathematics and Artificial Intelligence
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Causation, action, and counterfactuals
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Identifying conditional causal effects
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION
Cybernetics and Systems
Identifying linear causal effects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Identifiability in causal Bayesian networks: a sound and complete algorithm
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
On the completeness of an identifiability algorithm for semi-Markovian models
Annals of Mathematics and Artificial Intelligence
Identifiability of path-specific effects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the testability of causal models with latent and instrumental variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
This paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.