Testing Identifiability of Causal Effects

2013/02/20 by Galles, David, Pearl, Judea
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1302.4948

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.

Related

• Identifying Conditional Causal Effects
• The Evaluation of Causal Effects in Studies with an Unobserved Exposure/Outcome Variable: Bounds and Identification
• Causal Identification in Time Series Models
• Identifiability of total effects from abstractions of time series causal graphs
• Identifiability by common backdoor in summary causal graphs of time series