# Mediation Analysis for Probabilities of Causation

arXiv: https://arxiv.org/abs/2412.14491 · HTML: https://mlsanity.com/paper/2412.14491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Probabilities of Causation for Continuous and Vector Variables](https://mlsanity.com/paper/2405.20487/) (2024)
- [Bounding the Probability of Causation in Mediation Analysis](https://mlsanity.com/paper/1411.2636/) (2014)
- [Mediated probabilities of causation](https://mlsanity.com/paper/2404.07397/) (2024)

## Built on by (most cited first, 2 of 2)

- [Decomposition of Probabilities of Causation with Two Mediators](https://mlsanity.com/paper/2505.04983/) (2025)
- [Same Predictions, Different Harms: Causal Auditing of Patient World Models](https://mlsanity.com/paper/2610.05198/) (2026)

## Abstract (arXiv)

Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset.