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

arXiv: https://arxiv.org/abs/1907.12912 · HTML: https://mlsanity.com/paper/1907.12912/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection](https://mlsanity.com/paper/2209.09188/) (2022)
- [BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect](https://mlsanity.com/paper/2211.10793/) (2022)
- [Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data](https://mlsanity.com/paper/2608.06288/) (2026)

## Abstract (arXiv)

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.