Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
arXiv:1302.4977
Abstract
The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a given plan can be predicted from passive observations on measured variables only. When the criterion is satisfied, a closed-form expression is provided for the probability that the plan will achieve a specified goal.
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
References in corpus (1)
Cited by in corpus (11)
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- Testing Identifiability of Causal Effects
- What Counterfactuals Can Be Tested
- Identification of Conditional Interventional Distributions
- Causal Inference by Surrogate Experiments: z-Identifiability
- Causal Reasoning in Graphical Time Series Models
- Identifying Dynamic Sequential Plans
- Identifying Conditional Causal Effects