# Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

## TL;DR

This paper investigates how competing events affect the estimation of heterogeneous treatment effects from time-to-event data, highlighting new challenges and considerations for causal inference in this context.

## Contribution

It introduces an analysis of the impact of competing events on HTE estimation, revealing additional covariate shift challenges and clarifying different causal effect definitions.

## Key findings

- Competing events can introduce covariate shift depending on the effect definition.
- Existing prediction models can be used as plug-in estimators for HTEs.
- Multiple causal effect definitions influence the impact of competing events.

## Abstract

We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compared to its counterparts studying HTE estimation without time-to-event data or competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We then investigate whether competing events present new challenges for HTE estimation -- in addition to the standard confounding problem --, and find that, because there are multiple definitions of causal effects in this setting -- namely total, direct and separable effects --, competing events can act as an additional source of covariate shift depending on the desired treatment effect interpretation and associated estimand. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.