Bounding the Probability of Causation in Mediation Analysis
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Abstract (arXiv)
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
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2014.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
- 2025Decomposition of Probabilities of Causation with Two Mediatorscited by 3
- 2024Mediation Analysis for Probabilities of Causationcited by 2
- 2026Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptionscited by 1
- 2026Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions21 Feb 2026
- 2025Decomposition of Probabilities of Causation with Two Mediators8 May 2025
- 2024Mediation Analysis for Probabilities of Causation19 Dec 2024
- 2024Probabilities of Causation for Continuous and Vector Variables30 May 2024