Search Results for author: Alejandro Schuler

Found 8 papers, 4 papers with code

Multivariate Probabilistic Regression with Natural Gradient Boosting

1 code implementation7 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.

Bayesian prognostic covariate adjustment

no code implementations24 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.

Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

no code implementations17 Dec 2020 Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher

Sample size reductions between 10% and 30% are attainable when using prognostic models that explain a clinically realistic percentage of the outcome variance.

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model

no code implementations2 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.

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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.

Weather Forecasting

A comparison of methods for model selection when estimating individual treatment effects

3 code implementations14 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.

Model Selection

Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset

no code implementations31 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.

Causal Inference

Some methods for heterogeneous treatment effect estimation in high-dimensions

1 code implementation1 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.

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