Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
On the theoretical limits to reliable causal inference
On the theoretical limits to reliable causal inference
Quantifier elimination for statistical problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models and exponential families
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in 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
Estimation of effects of sequential treatments by reparameterizing directed acyclic graphs
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
New d-separation identification results for learning continuous latent variable models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Inference in multi-agent causal models
International Journal of Approximate Reasoning
IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION
Cybernetics and Systems
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
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
| Hi-index | 0.01 |
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.