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Found 5 papers, 2 papers with code
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning.
Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional
As an alternative, the front-door criterion offers a solution, even in the presence of unmeasured confounders between treatment and outcome.
A Huber loss-based super learner with applications to healthcare expenditures
Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost estimation and has shown benefits over a single model.
Nonparametric inference for interventional effects with multiple mediators
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal.
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both.
