# Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation

arXiv: https://arxiv.org/abs/2106.11009 · HTML: https://mlsanity.com/paper/2106.11009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Nonparametric Conditional Local Independence Testing](https://mlsanity.com/paper/2203.13559/) (2022)
- [Stabilized Neural Prediction of Potential Outcomes in Continuous Time](https://mlsanity.com/paper/2410.03514/) (2024)
- [Automated Efficient Estimation using Monte Carlo Efficient Influence Functions](https://mlsanity.com/paper/2403.00158/) (2024)
- [Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review](https://mlsanity.com/paper/2303.07329/) (2023)
- [Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers](https://mlsanity.com/paper/2609.26142/) (2026)
- [Causal inference for the expected number of recurrent events in the presence of a terminal event](https://mlsanity.com/paper/2306.16571/) (2023)
- [Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions](https://mlsanity.com/paper/2607.03824/) (2026)

## Abstract (arXiv)

Targeted maximum likelihood estimation is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for estimation of causal parameters. Proposed targeted maximum likelihood procedures for survival and competing risks analysis have so far focused on events taken values in discrete time. We here present a targeted maximum likelihood estimation procedure for event times that take values in R+. We focuson the estimation of intervention-specific mean outcomes with stochastic interventions on a time-fixed treatment. For data-adaptive estimation of nuisance parameters, we propose a new flexible highly adaptive lasso estimation method for continuous-time intensities that can be implemented with L1-penalized Poisson regression. In a simulation study the targeted maximum likelihood estimator based on the highly adaptive lasso estimator proves to be unbiased and achieve proper coverage in agreement with the asymptotic theory and further displays efficiency improvements relative to a Kaplan-Meier approach.