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Found 11 papers, 4 papers with code
RieszBoost: Gradient Boosting for Riesz Regression
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.
Highly Adaptive Ridge
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.
Lassoed Tree Boosting
Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets.
Multivariate Probabilistic Regression with Natural Gradient Boosting
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
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
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.
Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model
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
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
A comparison of methods for model selection when estimating individual treatment effects
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.
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
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.
Some methods for heterogeneous treatment effect estimation in high-dimensions
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.
