Testing Identifiability of Causal Effects

Abstract

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.

Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)

References in corpus (1)

• Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables

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• Identifying causal effects in maximally oriented partially directed acyclic graphs
• Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
• Obtaining Causal Information by Merging Datasets with MAXENT
• Minimal enumeration of all possible total effects in a Markov equivalence class