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 Professors Marco Carone and Alex Luedtke.
My research interests encompass a wide range of areas, including semiparametric statistics, shape-constrained inference, distribution-free statistical learning and calibration, and inference after model selection. As a significant component of my research, I focus on developing distribution-free causal inference methods that demonstrate robustness to treatment positivity/overlap violations and yield less variable estimates and narrower confidence intervals compared to standard nonparametric approaches. I am enthusiastic about applying these methodologies to various domains, such as survival and longitudinal data analysis, observational studies, 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. Additionally, you can access all my research publications on my Google Scholar profile. To explore a selection of my works, please visit the publications tab.