# Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review arXiv: https://arxiv.org/abs/2303.07329 ยท HTML: https://mlsanity.com/paper/2303.07329/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 217 references that are in the map. - First posted on arXiv 13 Mar 2023. ## Builds on (most cited first, 24 of 24) - [Non-parametric causal effects based on longitudinal modified treatment policies](https://mlsanity.com/paper/2006.01366/) (2020) - [Machine learning for causal inference: on the use of cross-fit estimators](https://mlsanity.com/paper/2004.10337/) (2020) - [Practical considerations for specifying a super learner](https://mlsanity.com/paper/2204.06139/) (2022) - [Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis](https://mlsanity.com/paper/1910.08877/) (2019) - [Nonparametric inverse probability weighted estimators based on the highly adaptive lasso](https://mlsanity.com/paper/2005.11303/) (2020) - [Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation](https://mlsanity.com/paper/2106.11009/) (2021) - [On Adaptive Propensity Score Truncation in Causal Inference](https://mlsanity.com/paper/1707.05861/) (2017) - [Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions](https://mlsanity.com/paper/1802.05005/) (2018) - [Targeting Learning: Robust Statistics for Reproducible Research](https://mlsanity.com/paper/2006.07333/) (2020) - [Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials](https://mlsanity.com/paper/2106.15737/) (2021) - [Doubly Robust Inference for Targeted Minimum Loss Based Estimation in Randomized Trials with Missing Outcome Data](https://mlsanity.com/paper/1704.01538/) (2017) - [Targeted Learning with Daily EHR Data](https://mlsanity.com/paper/1705.09874/) (2017) - [A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure](https://mlsanity.com/paper/1706.02675/) (2017) - [Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials](https://mlsanity.com/paper/2003.13771/) (2020) - [Nonparametric causal mediation analysis for stochastic interventional (in)direct effects](https://mlsanity.com/paper/2009.06203/) (2020) - [Evaluating the Impact of State-Level Public Masking Mandates on New COVID-19 Cases and Deaths in the United States: A Demonstration of the Causal Roadmap](https://mlsanity.com/paper/2110.06115/) (2021) - [Scalable Collaborative Targeted Learning for High-Dimensional Data](https://mlsanity.com/paper/1703.02237/) (2017) - [Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data](https://mlsanity.com/paper/1706.10029/) (2017) - [Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting](https://mlsanity.com/paper/1707.09021/) (2017) - [Complier stochastic direct effects: identification and robust estimation](https://mlsanity.com/paper/1810.12452/) (2018) - [Modeling Treatment Effect Modification in Multidrug-Resistant Tuberculosis in an Individual Patient Data Meta-Analysis](https://mlsanity.com/paper/2101.03997/) (2021) - [A Primer on Causality in Data Science](https://mlsanity.com/paper/1809.02408/) (2018) - [Efficient nonparametric estimation of the covariate-adjusted threshold-response function, a support-restricted stochastic intervention](https://mlsanity.com/paper/2107.11459/) (2021) - [Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures](https://mlsanity.com/paper/2110.12529/) (2021) ## Built on by (most cited first, 3 of 3) - [Targeted Learning for Variable Importance](https://mlsanity.com/paper/2411.02221/) (2024) - [Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity](https://mlsanity.com/paper/2510.22202/) (2025) - [Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning](https://mlsanity.com/paper/2512.13610/) (2025) ## Abstract (arXiv) The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarised the epidemiological discipline, geographical location, expertise of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. Of the 81 publications included, 25% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By the first half of 2022, 70% of the publications originated from outside the United States and explored up to 7 different epidemiological disciplines in 2021-22. Double-robustness, bias reduction and model misspecification were the main motivations that drew researchers towards the TMLE framework. Through time, a wide variety of methodological, tutorial and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits, and adoption, of TMLE.