Mediation Analysis for Probabilities of Causation

TL;DR

This paper introduces new variants of probabilities of causation that quantify the necessity and sufficiency of treatments, providing identification theorems and demonstrating their application on real data.

Contribution

It presents novel PoC measures for different causal pathways, along with identification theorems enabling their estimation from observational data.

Findings

New PoC variants for direct and indirect effects

Identification theorems for observational data

Application to real-world psychology dataset

Abstract

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.