Bounding the Probability of Causation in Mediation Analysis
This work addresses the challenge of quantifying causal probabilities in mediation scenarios, which is incremental as it builds on existing bounds with specific conditions.
The paper tackles the problem of bounding the probability of causation in mediation analysis, showing how bounds can be adapted or improved with additional information, particularly when a mediating variable is observed, and provides a new analysis for cases with no direct effect and no confounding.
Given empirical evidence for the dependence of an outcome variable on an exposure variable, we can typically only provide bounds for the "probability of causation" in the case of an individual who has developed the outcome after being exposed. We show how these bounds can be adapted or improved if further information becomes available. In addition to reviewing existing work on this topic, we provide a new analysis for the case where a mediating variable can be observed. In particular we show how the probability of causation can be bounded when there is no direct effect and no confounding. Keywords: Causal inference, Mediation Analysis, Probability of Causation