On treating right-censoring events like treatments

TL;DR

This paper demonstrates that well-defined causal estimands can be formulated without eliminating all right-censoring events, by classifying these events more precisely from a causal perspective.

Contribution

It introduces a framework that distinguishes types of right-censoring events, clarifying their role in causal inference and linking them to classical survival analysis assumptions.

Findings

A new classification of right-censoring events from a causal perspective

Framework connecting censoring definitions with causal estimand identification

Guidance for handling right-censoring in causal inference

Abstract

In causal inference literature, potential outcomes are often indexed by the "elimination of all right-censoring events," leading to the perception that such a restriction is necessary for defining well-posed causal estimands. In this paper, we clarify that this restriction is not required: a well-defined estimand can be formulated without indexing on the elimination of such events. Achieving this requires a more precise classification of right-censoring events than has historically been considered, as the nature of these events has direct implications for identification of the target estimand. We provide a framework that distinguishes different types of right-censoring events from a causal perspective, and demonstrate how this framework relates to censoring definitions and assumptions in classical survival analysis literature. By bridging these perspectives, we provide a clearer understanding of how to handle right-censoring events and provide guidance for identifying causal estimands when right-censored events are present.