Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Studies in causal reasoning and learning
Studies in causal reasoning and learning
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
Journal of Artificial Intelligence Research
Identifiability of path-specific effects
IJCAI'05 Proceedings of the 19th international joint conference on 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
Introduction to Causal Inference
The Journal of Machine Learning Research
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
Facilitating score and causal inference trees for large observational studies
The Journal of Machine Learning Research
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We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; cause-effect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple "parallel worlds" and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.