Mark van der Laan

7.7k total citations · 2 hit papers
96 papers, 4.6k citations indexed

About

Mark van der Laan is a scholar working on Statistics and Probability, Economics and Econometrics and Artificial Intelligence. According to data from OpenAlex, Mark van der Laan has authored 96 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Statistics and Probability, 13 papers in Economics and Econometrics and 10 papers in Artificial Intelligence. Recurrent topics in Mark van der Laan's work include Advanced Causal Inference Techniques (34 papers), Statistical Methods and Inference (32 papers) and Statistical Methods and Bayesian Inference (28 papers). Mark van der Laan is often cited by papers focused on Advanced Causal Inference Techniques (34 papers), Statistical Methods and Inference (32 papers) and Statistical Methods and Bayesian Inference (28 papers). Mark van der Laan collaborates with scholars based in United States, Netherlands and United Kingdom. Mark van der Laan's co-authors include Nicholas P. Jewell, Jennifer Ahern, Maya Petersen, Alan Hubbard, William A. Satariano, Sheri A. Lippman, Tim A. Bruckner, Nancy L. Fleischer, Cheng Ju and Romain Neugebauer and has published in prestigious journals such as Science, The Lancet and Journal of Biological Chemistry.

In The Last Decade

Mark van der Laan

91 papers receiving 4.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

To GEE or Not to GEE

2010 863 citations Alan Hubbard, Mark van der Laan et al. Epidemiology profile →
Promoting Transparency in Social Science Research

2014 298 citations Mark van der Laan et al. profile →

Peers

Countries citing papers authored by Mark van der Laan

Since Specialization

Citations

This map shows the geographic impact of Mark van der Laan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark van der Laan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark van der Laan more than expected).

Fields of papers citing papers by Mark van der Laan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark van der Laan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark van der Laan. The network helps show where Mark van der Laan may publish in the future.

Co-authorship network of co-authors of Mark van der Laan

This figure shows the co-authorship network connecting the top 25 collaborators of Mark van der Laan. A scholar is included among the top collaborators of Mark van der Laan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark van der Laan. Mark van der Laan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Challenges and Possible Strategies to Address Them in Rare Disease Drug Development: A Statistical Perspective 2025 ·Clinical Pharmacology & Therapeutics ·(unknown), Lei Nie, Shiowjen Lee, Haitao Chu, (unknown), (unknown), Weili He, Thomas Jemielita, Susan Gruber, (unknown), Roy Tamura, (unknown), Yihua Zhao, Yong Chen, Mark van der Laan, (unknown)	1
2	Use of Real‐World Data and Real‐World Evidence in Rare Disease Drug Development: A Statistical Perspective 2025 ·Clinical Pharmacology & Therapeutics ·(unknown), Susan Gruber, (unknown), Haitao Chu, Shiowjen Lee, Haijun Tian, Yan Wang, Weili He, Thomas Jemielita, (unknown), Roy Tamura, Tian Lu, Yihua Zhao, Yong Chen, Mark van der Laan, Lei Nie	2
3	The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications 2024 ·Epidemiology ·Nerissa Nance, Maya Petersen, Mark van der Laan, Laura B. Balzer	1
4	Extension of efficacy range for targeted malaria-elimination interventions due to spillover effects 2024 ·Nature Medicine ·Jade Benjamin‐Chung, Haodong Li, Anna Nguyen, (unknown), (unknown), Henry Ntuku, Lisa M. Prach, Munyaradzi Tambo, Lindsey Wu, Chris Drakeley, Roly Gosling, Davis Mumbengegwi, Immo Kleinschmidt, Jennifer L. Smith, Alan Hubbard, Mark van der Laan, Michelle S. Hsiang	3
5	Artificial Intelligence–Based Copilots to Generate Causal Evidence 2024 ·NEJM AI ·Maya L. Petersen, Ahmed M. Alaa, Emre Kıcıman, Chris Holmes, Mark van der Laan	2
6	Skeletal muscle mitochondrial fragmentation predicts age‐associated decline in physical capacity 2024 ·Aging Cell ·Richie P. Goulding, Braeden T. Charlton, (unknown), Mark van der Laan, (unknown), Wendy Noort, (unknown), Brent Appelman, Michèle van Vugt, Anita E. Grootemaat, Nicole N. van der Wel, Jos J. de Koning, Frank W. Bloemers, Rob C. I. Wüst	2
7	CVtreeMLE: Efficient Estimation of Mixed Exposuresusing Data Adaptive Decision Trees and Cross-Validated Targeted MaximumLikelihood Estimation in R 2023 ·The Journal of Open Source Software ·(unknown), Alan Hubbard, Mark van der Laan	0
8	A fundamental measure of treatment effect heterogeneity 2021 ·SHILAP Revista de lepidopterología ·Jonathan Levy, Mark van der Laan, Alan Hubbard, Romain Pirracchio	7
9	Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials 2021 ·Biostatistics ·Laura B. Balzer, Mark van der Laan, James Ayieko, Moses R. Kamya, Gabriel Chamie, Joshua Schwab, Diane V. Havlir, Maya Petersen	27
10	cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R 2021 ·The Journal of Open Source Software ·(unknown), Nima S. Hejazi, (unknown), Mark van der Laan, Sandrine Dudoit	5
11	hal9001: Scalable highly adaptive lasso regression in R 2020 ·The Journal of Open Source Software ·Nima S. Hejazi, Jeremy Coyle, Mark van der Laan	14
12	Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator 2020 ·The International Journal of Biostatistics ·Weixin Cai, Mark van der Laan	57
13	Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda 2019 ·Clinical Infectious Diseases ·Laura B. Balzer, Diane V. Havlir, Moses R. Kamya, Gabriel Chamie, Edwin D. Charlebois, Tamara D. Clark, Catherine A. Koss, Dalsone Kwarisiima, James Ayieko, Norton Sang, Jane Kabami, Mucunguzi Atukunda, Vivek Jain, Carol S. Camlin, Craig R. Cohen, Elizabeth A. Bukusi, Mark van der Laan, Maya L. Petersen	56
14	Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study 2019 ·The International Journal of Biostatistics ·Linh Tran, Constantin T. Yiannoutsos, Kara Wools‐Kaloustian, Abraham Siika, Mark van der Laan, Maya L. Petersen	23
15	Smoking Is Associated with Higher Disease Activity in Rheumatoid Arthritis: A Longitudinal Study Controlling for Time-varying Covariates 2018 ·The Journal of Rheumatology ·Milena Gianfrancesco, Laura Trupin, Stephen Shiboski, Mark van der Laan, Jonathan Graf, John B. Imboden, Jinoos Yazdany, Gabriela Schmajuk	27
16	A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso 2017 ·The International Journal of Biostatistics ·Mark van der Laan	44
17	One-Step Targeted Minimum Loss-based Estimation Based on Universal Least Favorable One-Dimensional Submodels 2016 ·The International Journal of Biostatistics ·Mark van der Laan, Susan Gruber	20
18	Statistics as a Science, Not an Art: The Way to Survive in Data 2015 ·Mark van der Laan	2
19	Improving Propensity Score Estimators' Robustness to Model Misspecification Using Super Learner 2014 ·American Journal of Epidemiology ·Romain Pirracchio, Maya Petersen, Mark van der Laan	122
20	Comment 2000 ·Journal of the American Statistical Association ·James M. Robins, Andrea Rotnitzky, Mark van der Laan	14

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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