Nolan Cole
@nolancole.bsky.social
Biostatistics phd student @University of Washington
Interested in non-parametric statistics, causal inference, and science!
Interested in non-parametric statistics, causal inference, and science!
Reposted by Nolan Cole
In this preprint, led by @nolancole.bsky.social (PhD student in Biostats at UW) and William (Undergrad! in BioSciences at Rice) we demonstrate how annotation choice and quantification method have significant downstream impacts on colocalization and TWAS results www.biorxiv.org/content/10.1...
Quantification method affects replicability of eQTL analysis, colocalization, and TWAS
eQTL mapping and TWAS are widely used to contextualize GWAS, yet the impact of RNA-seq processing choices remains unexplored. We find that RNA-seq quantification method and transcriptomic reference su...
www.biorxiv.org
September 2, 2025 at 2:36 PM
In this preprint, led by @nolancole.bsky.social (PhD student in Biostats at UW) and William (Undergrad! in BioSciences at Rice) we demonstrate how annotation choice and quantification method have significant downstream impacts on colocalization and TWAS results www.biorxiv.org/content/10.1...
More great work from Lars at UW!
Had a great time presenting at #ACIC on doubly robust inference via calibration
Calibrating nuisance estimates in DML protects against model misspecification and slow convergence.
Just one line of code is all it takes.
Calibrating nuisance estimates in DML protects against model misspecification and slow convergence.
Just one line of code is all it takes.
May 19, 2025 at 3:46 AM
More great work from Lars at UW!
Reposted by Nolan Cole
Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!
Lars van der Laan, Aurelien Bibaut, Nathan Kallus, Alex Luedtke
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
January 22, 2025 at 1:54 PM
Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!
Reposted by Nolan Cole
Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aur\'elien Bibaut
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
January 14, 2025 at 5:40 AM
Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aur\'elien Bibaut
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926