# Causal inference with multi-state models - estimands and estimators of the population-attributable fraction

## TL;DR

This paper revises the definition of the population-attributable fraction (PAF) for complex time-to-event data, clarifying estimands and estimators to improve causal interpretation in epidemiological studies.

## Contribution

It provides a clear conceptual framework for defining and estimating PAF in complex time-to-event data, extending previous approaches.

## Key findings

- Revised PAF definitions for time-to-event data
- Clarified distinction between estimands and estimators
- Proposed methods for causal interpretation of PAF

## Abstract

The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by a proper differentiation between estimands and estimators as well as causal effect measures and measures of association.