Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Testing identifiability of causal effects
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Identifying conditional causal effects
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Inference in multi-agent causal models
International Journal of Approximate Reasoning
Causal analysis for performance modeling of computer programs
Scientific Programming
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
Identification of joint interventional distributions in recursive semi-Markovian causal models
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
Identifying direct causal effects in linear models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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
Effects of treatment on the treated: identification and generalization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Causal analysis with Chain Event Graphs
Artificial Intelligence
The algorithmization of counterfactuals
Annals of Mathematics and Artificial Intelligence
On the testable implications of causal models with hidden variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Causal identifiability via Chain Event Graphs
Artificial Intelligence
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This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called "causal graph", in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion for the effects of a singleton variable on any set of variables.