Identification of regulatory elements using a feature selection method. - Sündüz Keles; Mark J. van der Laan; Michael B. Eisen (Bioinform., 2002)

Current Status Data: Review, Recent Developments and Open Problems. - Nicholas P. Jewell; Mark J. van der Laan (Advances in Survival Analysis, 2003)

Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures. - Sündüz Keles; Mark J. van der Laan; James M. Robins (Advances in Survival Analysis, 2003)

Multiple Testing for Gene Expression Data: An Investigation of Null Distributions with Consequences for the Permutation Test. - Katherine S. Pollard; Mark J. van der Laan (METMBS, 2003)

Fitting of mixtures with unspecified number of components using cross validation distance estimate. - Maja Miloslavsky; Mark J. van der Laan (Comput. Stat. Data Anal., 2003)

Loss-based estimation with cross-validation: applications to microarray data analysis. - Sandrine Dudoit; Mark J. van der Laan; Sündüz Keles; Annette M. Molinaro; Sandra E. Sinisi; Siew Leng Teng (SIGKDD Explor., 2003)

Regulatory motif finding by logic regression. - Sündüz Keles; Mark J. van der Laan; Chris Vulpe (Bioinform., 2004)

Data-adaptive test statistics for microarray data. - Sach Mukherjee; Stephen J. Roberts; Mark J. van der Laan (ECCB/JBI, 2005)

A causal inference approach for constructing transcriptional regulatory networks. - Biao Xing; Mark J. van der Laan (Bioinform., 2005)

A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data. - Biao Xing; Mark J. van der Laan (J. Comput. Biol., 2005)

A semiparametric model selection criterion with applications to the marginal structural model. - M. Alan Brookhart; Mark J. van der Laan (Comput. Stat. Data Anal., 2006)

Causal effects in longitudinal studies: Definition and maximum likelihood estimation. - Romain Neugebauer; Mark J. van der Laan (Comput. Stat. Data Anal., 2006)

G-computation estimation for causal inference with complex longitudinal data. - Romain Neugebauer; Mark J. van der Laan (Comput. Stat. Data Anal., 2006)

Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data. - Sündüz Keles; Mark J. van der Laan; Sandrine Dudoit; Simon E. Cawley (J. Comput. Biol., 2006)

partDSA: deletion/substitution/addition algorithm for partitioning the covariate space in prediction. - Annette M. Molinaro; Karen Lostritto; Mark J. van der Laan (Bioinform., 2010)

A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models. - Thaddeus J. Haight; Yue Wang; Mark J. van der Laan; Ira B. Tager (Comput. Stat. Data Anal., 2010)

Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. - Hui Wang; Mark J. van der Laan (BMC Bioinform., 2011)

Tutorial for Causal Inference. - Laura Balzer; Maya Petersen; Mark J. van der Laan (Handbook of Big Data, 2016)

Mining with Inference: Data-Adaptive Target Parameters. - Alan E. Hubbard; Mark J. van der Laan (Handbook of Big Data, 2016)

The Highly Adaptive Lasso Estimator. - David C. Benkeser; Mark J. van der Laan (DSAA, 2016)

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification. - Cheng Ju; Aurélien Bibaut; Mark J. van der Laan (CoRR, 2017)

Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series. - Mark J. van der Laan; Ivana Malenica (CoRR, 2018)

methyvim: Targeted, robust, and model-free differential methylation analysis in R. - Nima S. Hejazi; Rachael V. Phillips; Alan E. Hubbard; Mark J. van der Laan (F1000Research, 2018)

More Efficient Off-Policy Evaluation through Regularized Targeted Learning. - Aurélien Bibaut; Ivana Malenica; Nikos Vlassis; Mark J. van der Laan (ICML, 2019)

Expert-Augmented Machine Learning. - Efstathios D. Gennatas; Jerome H. Friedman; Lyle H. Ungar; Romain Pirracchio; Eric Eaton; L. Reichman; Yannet Interian; Charles B. Simone II; A. Auerbach; E. Delgado; Mark J. van der Laan; Timothy D. Solberg; et al. (CoRR, 2019)

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. - Aurélien Bibaut; Antoine Chambaz; Mark J. van der Laan (UAI, 2020)

Rate-adaptive model selection over a collection of black-box contextual bandit algorithms. - Aurélien F. Bibaut; Antoine Chambaz; Mark J. van der Laan (CoRR, 2020)

hal9001: Scalable highly adaptive lasso regression in R. - Nima S. Hejazi; Jeremy R. Coyle; Mark J. van der Laan (J. Open Source Softw., 2020)

Post-Contextual-Bandit Inference. - Aurélien Bibaut; Maria Dimakopoulou; Nathan Kallus; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. - Aurélien Bibaut; Nathan Kallus; Maria Dimakopoulou; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Adaptive Sequential Design for a Single Time-Series. - Ivana Malenica; Aurélien Bibaut; Mark J. van der Laan (CoRR, 2021)

Personalized Online Machine Learning. - Ivana Malenica; Rachael V. Phillips; Romain Pirracchio; Antoine Chambaz; Alan E. Hubbard; Mark J. van der Laan (CoRR, 2021)

Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. - Mark J. van der Laan; Sherri Rose (CoRR, 2021)

cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R. - Philippe Boileau; Nima S. Hejazi; Brian Collica; Mark J. van der Laan; Sandrine Dudoit (J. Open Source Softw., 2021)

The Selectively Adaptive Lasso. - Alejandro Schuler; Mark J. van der Laan (CoRR, 2022)

'haldensify': Highly adaptive lasso conditional density estimation in 'R'. - Nima S. Hejazi; Mark J. van der Laan; David C. Benkeser (J. Open Source Softw., 2022)

The Conditional Super Learner. - Gilmer Valdes; Yannet Interian; Efstathios D. Gennatas; Mark J. van der Laan (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. - Ahmed M. Alaa; Zaid Ahmad; Mark J. van der Laan (NeurIPS, 2023)

Multi-task Highly Adaptive Lasso. - Ivana Malenica; Rachael V. Phillips; Daniel Lazzareschi; Jeremy R. Coyle; Romain Pirracchio; Mark J. van der Laan (CoRR, 2023)

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. - Ahmed Alaa; Zaid Ahmad; Mark J. van der Laan (CoRR, 2023)

CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R. - David McCoy; Alan E. Hubbard; Mark J. van der Laan (J. Open Source Softw., 2023)

Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions. - Philippe Boileau; Nima S. Hejazi; Mark J. van der Laan; Sandrine Dudoit (J. Comput. Graph. Stat., 2023)

SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. - David McCoy; Alejandro Schuler; Alan E. Hubbard; Mark J. van der Laan (J. Open Source Softw., 2023)

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. - Toru Shirakawa; Yi Li; Yulun Wu; Sky Qiu; Yuxuan Li; Mingduo Zhao; Hiroyasu Iso; Mark J. van der Laan (ICML, 2024)

Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning. - Razieh Nabi; Nima S. Hejazi; Mark J. van der Laan; David C. Benkeser (CoRR, 2024)

Large Language Models as Co-Pilots for Causal Inference in Medical Studies. - Ahmed Alaa; Rachael V. Phillips; Emre Kiciman; Laura Balzer; Mark J. van der Laan; Maya Petersen (CoRR, 2024)

Highly Adaptive Ridge. - Alejandro Schuler; Alexander Hagemeister; Mark J. van der Laan (CoRR, 2024)

Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks. - Yi Li; David McCoy; Nolan Gunter; Kaitlyn Lee; Alejandro Schuler; Mark J. van der Laan (CoRR, 2025)