Testing Identifiability of Causal Effects
This addresses the challenge of causal inference in complex systems with hidden variables, offering a systematic approach for researchers and practitioners.
The paper tackles the problem of identifying causal effects in the presence of unmeasured variables, showing that identification can be done systematically in polynomial time and providing closed-form expressions for probability calculations.
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.