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Found 7 papers, 5 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.
A framework for causal segmentation analysis with machine learning in large-scale digital experiments
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digital experiments.
Targeting Learning: Robust Statistics for Reproducible Research
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence.
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at $n^{-1/3}$-rate to the true weighting mechanism.
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials
We propose nonparametric methodology for efficiently estimating a counterfactual parameter that quantifies the impact of a given immune response marker on the subsequent probability of infection.
Methodology
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational biology and allied sciences.
Methodology
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses.
