Mark J. van der Laan

20.6k total citations · 3 hit papers
317 papers, 12.1k citations indexed

About

Mark J. van der Laan is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Mark J. van der Laan has authored 317 papers receiving a total of 12.1k indexed citations (citations by other indexed papers that have themselves been cited), including 252 papers in Statistics and Probability, 42 papers in Artificial Intelligence and 36 papers in Molecular Biology. Recurrent topics in Mark J. van der Laan's work include Statistical Methods and Inference (193 papers), Advanced Causal Inference Techniques (165 papers) and Statistical Methods and Bayesian Inference (143 papers). Mark J. van der Laan is often cited by papers focused on Statistical Methods and Inference (193 papers), Advanced Causal Inference Techniques (165 papers) and Statistical Methods and Bayesian Inference (143 papers). Mark J. van der Laan collaborates with scholars based in United States, Denmark and France. Mark J. van der Laan's co-authors include Sandrine Dudoit, Alan Hubbard, Susan Gruber, Eric C. Polley, Maya L. Petersen, Sherri Rose, Daniel B. Rubin, Katherine S. Pollard, Sandra E. Sinisi and Maya Petersen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Mark J. van der Laan

312 papers receiving 11.5k citations

Hit Papers

align trajectories sort by overperformance

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.

Super Learner

2007 1.2k citations Mark J. van der Laan, Eric C. Polley et al. Statistical Applications in Genetics and Molecular Biology profile →
Targeted Maximum Likelihood Learning

2006 510 citations Mark J. van der Laan, Daniel B. Rubin The International Journal of Biostatistics profile →
Targeted Learning

2011 489 citations Mark J. van der Laan et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark J. van der Laan United States	55	6.9k	1.7k	1.4k	1.2k	751	317	12.1k
L. J. Wei United States	49	5.7k 0.8×	1.4k 0.8×	1.0k 0.7×	842 0.7×	1.3k 1.7×	123	13.1k
Anastasios A. Tsiatis United States	60	7.8k 1.1×	1.8k 1.1×	914 0.7×	671 0.6×	735 1.0×	161	12.2k
D. Y. Lin United States	50	6.1k 0.9×	2.3k 1.4×	889 0.6×	936 0.8×	1.3k 1.7×	146	15.0k
Geert Verbeke Belgium	50	4.3k 0.6×	904 0.5×	1.3k 0.9×	1.2k 1.0×	824 1.1×	312	14.2k
Thomas A. Louis United States	57	4.0k 0.6×	1.7k 1.0×	1.4k 1.0×	848 0.7×	1.1k 1.4×	242	15.2k
Geert Molenberghs Belgium	71	8.8k 1.3×	2.6k 1.5×	2.0k 1.4×	1.0k 0.9×	1.8k 2.4×	682	24.1k
Xiao‐Hua Zhou United States	64	2.9k 0.4×	1.2k 0.7×	1.7k 1.2×	1.7k 1.4×	1.6k 2.2×	540	17.1k
Joseph G. Ibrahim United States	60	7.8k 1.1×	1.3k 0.8×	2.8k 2.1×	3.1k 2.7×	829 1.1×	397	16.6k
John P. Klein United States	67	3.8k 0.6×	1.3k 0.8×	718 0.5×	1.2k 1.0×	1.3k 1.8×	279	21.6k
Jeremy M. G. Taylor United States	72	4.6k 0.7×	919 0.5×	1.7k 1.2×	5.1k 4.4×	1.6k 2.1×	402	22.0k

Countries citing papers authored by Mark J. van der Laan

Since Specialization

Citations

This map shows the geographic impact of Mark J. 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 J. 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 J. van der Laan more than expected).

Fields of papers citing papers by Mark J. van der Laan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark J. 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 J. van der Laan. The network helps show where Mark J. van der Laan may publish in the future.

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

This figure shows the co-authorship network connecting the top 25 collaborators of Mark J. van der Laan. A scholar is included among the top collaborators of Mark J. 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 J. van der Laan. Mark J. 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

Laan, Mark J. van der, et al.. (2024). Semiparametric efficient estimation of small genetic effects in large-scale population cohorts. Biostatistics. 26(1).

Tran, Linh, Maya L. Petersen, Joshua Schwab, & Mark J. van der Laan. (2023). Robust variance estimation and inference for causal effect estimation. SHILAP Revista de lepidopterología. 11(1). 6 indexed citations

Balzer, Laura B., James Ayieko, Dalsone Kwarisiima, et al.. (2020). Far from MCAR. Epidemiology. 31(5). 620–627. 7 indexed citations

Neugebauer, Romain, Julie A. Schmittdiel, Alyce S. Adams, Richard W. Grant, & Mark J. van der Laan. (2017). Identification of the Joint Effect of a Dynamic Treatment Intervention and a Stochastic Monitoring Intervention Under the No Direct Effect Assumption. SHILAP Revista de lepidopterología. 5(1). 6 indexed citations

Rudolph, Kara E., Oleg Sofrygin, Wenjing Zheng, & Mark J. van der Laan. (2017). Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting. arXiv (Cornell University). 3 indexed citations

Sofrygin, Oleg & Mark J. van der Laan. (2016). Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population. SHILAP Revista de lepidopterología. 5(1). 30 indexed citations

Lendle, Samuel, Bruce Fireman, & Mark J. van der Laan. (2015). Balancing Score Adjusted Targeted Minimum Loss-based Estimation. SHILAP Revista de lepidopterología. 3(2). 139–155. 3 indexed citations

Petersen, Maya, Erin LeDell, Joshua Schwab, et al.. (2015). Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring. JAIDS Journal of Acquired Immune Deficiency Syndromes. 69(1). 109–118. 42 indexed citations

Weber, Ann M., Mark J. van der Laan, & Maya L. Petersen. (2014). Assumption Trade-Offs When Choosing Identification Strategies for Pre-Post Treatment Effect Estimation: An Illustration of a Community-Based Intervention in Madagascar. SHILAP Revista de lepidopterología. 3(1). 109–130. 4 indexed citations

10.

Laan, Mark J. van der & Alexander R. Luedtke. (2014). Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule. SHILAP Revista de lepidopterología. 3(1). 61–95. 48 indexed citations

11.

Laan, Mark J. van der. (2014). Causal Inference for a Population of Causally Connected Units. SHILAP Revista de lepidopterología. 2(1). 13–74. 38 indexed citations

12.

Petersen, Maya & Mark J. van der Laan. (2014). Causal Models and Learning from Data. Epidemiology. 25(3). 418–426. 139 indexed citations

13.

Laan, Mark J. van der, Maya L. Petersen, & Wenjing Zheng. (2013). Estimating the Effect of a Community-Based Intervention with Two Communities. SHILAP Revista de lepidopterología. 1(1). 83–106. 6 indexed citations

14.

Díaz, Iván & Mark J. van der Laan. (2013). Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions. The International Journal of Biostatistics. 9(2). 161–174. 17 indexed citations

15.

Díaz, Iván & Mark J. van der Laan. (2013). Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems. The International Journal of Biostatistics. 9(2). 149–160. 29 indexed citations

16.

Laan, Mark J. van der & Susan Gruber. (2012). Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions. The International Journal of Biostatistics. 8(1). 111 indexed citations

17.

Gruttola, Victor De, et al.. (2012). A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis. The International Journal of Biostatistics. 8(1). 35 indexed citations

18.

Laan, Mark J. van der, et al.. (2010). Statistics Ready for a Revolution: Next Generation of Statisticians Must Build Tools for Massive Data Sets. 38–39. 16 indexed citations

19.

Laan, Mark J. van der, Eric C. Polley, & Alan Hubbard. (2007). Super Learner. Statistical Applications in Genetics and Molecular Biology. 6(1). Article25–Article25. 1174 indexed citations breakdown →

20.

Laan, Mark J. van der & Daniel B. Rubin. (2006). Targeted Maximum Likelihood Learning. The International Journal of Biostatistics. 2(1). 510 indexed citations breakdown →

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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