no code implementations • 8 Jan 2025 • Kaitlyn J. Lee, Alejandro Schuler
Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy.
no code implementations • 3 Oct 2024 • Alejandro Schuler, Alexander Hagemeister, Mark van der Laan
In this paper we propose the Highly Adaptive Ridge (HAR): a regression method that achieves a $n^{-1/3}$ dimension-free L2 convergence rate in the class of right-continuous functions with square-integrable sectional derivatives.
no code implementations • 22 May 2022 • Alejandro Schuler, Yi Li, Mark van der Laan
Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets.
2 code implementations • 7 Jun 2021 • Michael O'Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler
Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
no code implementations • 24 Dec 2020 • David Walsh, Alejandro Schuler, Diana Hall, Jon Walsh, Charles Fisher
Here we go further, utilizing a Bayesian framework that combines prognostic covariate adjustment with an empirical prior distribution learned from the predictive performances of the prognostic model on past trials.
no code implementations • 17 Dec 2020 • Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher
When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model above and beyond the linear relationship with the raw covariates.
no code implementations • 2 Jun 2020 • Alejandro Schuler, Aashish Bhardwaj, Vincent Liu
Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices.
4 code implementations • ICML 2020 • Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
3 code implementations • 14 Apr 2018 • Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, Nigam Shah
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
no code implementations • 31 Oct 2017 • Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah
Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.
1 code implementation • 1 Jul 2017 • Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H. Shah, Trevor Hastie, Robert Tibshirani
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.