# On the estimation of average treatment effects with right-censored time to event outcome and competing risks

## TL;DR

This paper develops doubly robust estimators for average treatment effects on right-censored time-to-event data with competing risks, providing asymptotic properties and empirical validation.

## Contribution

It introduces new doubly robust estimation equations for causal inference in right-censored competing risks data, with theoretical guarantees and practical implementation.

## Key findings

- Estimators are asymptotically linear and regular.
- Simulation studies show robustness and accurate confidence intervals.
- Application to Danish registry data demonstrates practical utility.

## Abstract

We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, the censoring and the treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that our estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.