Lars van der Laan

University of Washington, Seattle. Department of Statistics

prof_pic.jpeg

B313

Padelford Hall, Northeast Stevens Way

Seattle, WA 98103

lvdlaan@uw.edu

About me

I am a third-year Ph.D. student in Statistics at the University of Washington, with a strong passion for exploring the intersections of causal inference and debiased machine learning. I am advised by Marco Carone, PhD and Alex Luedtke, PhD. For more detail on my background, please check out my CV.

My research interests encompass a wide range of areas, including semi/nonparametric statistics, shape-constrained inference, distribution-free statistical learning and calibration, and inference after data-driven model selection. I am enthusiastic about applying these methodologies to various domains, such as survival and longitudinal data analysis, inference on heterogeneous treatment effects, and personalized decision-making.

For the latest updates on my research and academic endeavors, you can follow me on Twitter at @Larsvanderlaan3 and connect with me on LinkedIn. Additionally, you can access all my research publications on my Google Scholar profile. To explore a curated selection of my works, please visit the publications tab.

selected publications

2024

  1. Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
    Mark Laan, Sky Qiu, and Lars Laan
    arXiv preprint arXiv:2405.07186, 2024
  2. Self-Calibrating Conformal Prediction
    Lars van der Laan, and Ahmed M. Alaa
    arXiv preprint arXiv:2402.07307, stat.ML, 2024
  3. Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
    Lars van der Laan, Marco Carone, and Alex Luedtke
    arXiv preprint arXiv:2402.01972, 2024

2023

  1. Adaptive debiased machine learning using data-driven model selection techniques
    Lars van der Laan, Marco Carone, Alex Luedtke, and 1 more author
    arXiv preprint arXiv:2307.12544, 2023
  2. Causal isotonic calibration for heterogeneous treatment effects
    Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, and 1 more author
    In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023

2022

  1. hal9001: The scalable highly adaptive lasso
    Jeremy R Coyle, Nima S Hejazi, Rachael V Phillips, and 2 more authors
    2022
    R package version 0.4.2